Optimizing spool and memory space management

ABSTRACT

A storage system has NVRAM (non-volatile random-access memory), solid-state storage memory, and a processor to perform a method. The method includes allocating virtual units of NVRAM with mapping of the virtual units to physical memory. The method includes writing data having various sizes into allocated first virtual units of memory and into allocated second virtual units of memory. The first virtual units of memory each include a first contiguous physical addressed amount of NVRAM having a first size. The second virtual units of memory each include an amount of NVRAM having a second size. The method includes relocating at least some of the data such that a portion of the allocated second virtual units of memory become available for the allocating.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims benefit of priority from U.S. patent applicationSer. No. 17/235,630 filed Apr. 20, 2021, which is a continuation-in-part(CIP) of U.S. patent application Ser. No. 16/524,491 filed Jul. 29,2019, which is a continuation of U.S. patent application Ser. No.15/359,225 filed Nov. 22, 2016, which claims benefit of ProvisionalApplication No. 62/415,398 filed Oct. 31, 2016, benefit of ProvisionalApplication No. 62/395,344 filed Sep. 15, 2016 and benefit ofProvisional Application No. 62/367,083 filed Jul. 26, 2016, each ofwhich are hereby incorporated by reference.

TECHNICAL FIELD

The technical field of the present disclosure relates to data storagesystems.

BACKGROUND

Solid-state memory, such as flash, is currently in use in solid-statedrives (SSD) to augment or replace conventional hard disk drives (HDD),writable CD (compact disk) or writable DVD (digital versatile disk)drives, collectively known as spinning media, and tape drives, forstorage of large amounts of data. Flash and other solid-state memorieshave characteristics that differ from spinning media. Such differentcharacteristics give rise to different needs for memory spacemanagement, data and metadata buffering and queuing, and communicationwithin a system. Scaling to larger amounts of memory, larger numbers ofstorage devices, and also multi chassis systems introduces furtherproblem areas and opportunities for optimization. Yet, many solid-statedrives are designed to conform to hard disk drive standards forcompatibility reasons, which makes it difficult to provide enhancedfeatures or take advantage of unique aspects of flash and othersolid-state memory. It is within this context that the embodimentsarise.

BRIEF DESCRIPTION OF THE DRAWINGS

The described embodiments and the advantages thereof may best beunderstood by reference to the following description taken inconjunction with the accompanying drawings. These drawings in no waylimit any changes in form and detail that may be made to the describedembodiments by one skilled in the art without departing from the spiritand scope of the described embodiments.

FIG. 1A illustrates a first example system for data storage inaccordance with some implementations.

FIG. 1B illustrates a second example system for data storage inaccordance with some implementations.

FIG. 1C illustrates a third example system for data storage inaccordance with some implementations.

FIG. 1D illustrates a fourth example system for data storage inaccordance with some implementations.

FIG. 2A is a perspective view of a storage cluster with multiple storagenodes and internal storage coupled to each storage node to providenetwork attached storage, in accordance with some embodiments.

FIG. 2B is a block diagram showing an interconnect switch couplingmultiple storage nodes in accordance with some embodiments.

FIG. 2C is a multiple level block diagram, showing contents of a storagenode and contents of one of the non-volatile solid state storage unitsin accordance with some embodiments.

FIG. 2D shows a storage server environment, which uses embodiments ofthe storage nodes and storage units of some previous figures inaccordance with some embodiments.

FIG. 2E is a blade hardware block diagram, showing a control plane,compute and storage planes, and authorities interacting with underlyingphysical resources, in accordance with some embodiments.

FIG. 2F depicts elasticity software layers in blades of a storagecluster, in accordance with some embodiments.

FIG. 2G depicts authorities and storage resources in blades of a storagecluster, in accordance with some embodiments.

FIG. 3A sets forth a diagram of a storage system that is coupled fordata communications with a cloud services provider in accordance withsome embodiments of the present disclosure.

FIG. 3B sets forth a diagram of a storage system in accordance with someembodiments of the present disclosure.

FIG. 3C sets forth an example of a cloud-based storage system inaccordance with some embodiments of the present disclosure.

FIG. 3D illustrates an exemplary computing device 350 that may bespecifically configured to perform one or more of the processesdescribed herein.

FIG. 4 illustrates an example of a fleet of storage systems 376 forproviding storage services.

FIG. 5 illustrates an example container system.

FIG. 6 is a block diagram of an embodiment of the space accounting logicshown in FIG. 3 in accordance with some embodiments.

FIG. 7 is an action diagram depicting migration from a storage unit,along with an incoming workload, directed by the space accounting logicand an allocator in a storage node in accordance with some embodiments.

FIG. 8 is a flow diagram of a method for evacuating or migrating datafrom a write group in accordance with some embodiments.

FIG. 9 depicts the space detector and the garbage collection move moduleof FIG. 6, with two alternatives for garbage collection to rebalance animbalance across the storage memory in accordance with some embodiments.

FIG. 10 is a flow diagram of a method for elective garbage collection instorage memory in accordance with some embodiments.

FIG. 11 depicts a storage system with spool insertion optimizing andstorage memory partitioning, in accordance with some embodiments.

FIG. 12 depicts an optimization of the storage system of FIG. 11 withspool insertion sizes devised to match storage memory allocation, inaccordance with an embodiment.

FIG. 13A depicts splitting a record to optimize towards a spoolinsertion size.

FIG. 13B depicts combining records to optimize towards a spool insertionsize.

FIG. 13C depicts combining commands to optimize towards a spoolinsertion size.

FIG. 13D depicts inlining to optimize towards a spool insertion size.

FIG. 13E depicts a further example of inlining to optimize towards aspool insertion size.

FIG. 14 illustrates a byte allocator in a spool insertion optimizer,accessing various buckets to compose spool insertions, in accordancewith an embodiment.

FIG. 15 illustrates authorities, spool allocation and storage memoryallocation for various optimizations for versions of the storage systemsof FIG. 11 and FIG. 12.

FIG. 16 illustrates a multi chassis storage system for variousoptimizations for versions of the storage systems of FIG. 11 and FIG.12.

FIG. 17 is a flow diagram of a method for memory space management, whichcan be practiced on or by embodiments described herein, variationsthereof, and further systems and storage systems.

FIG. 18 illustrates a storage system that allocates virtual units ofmemory and defragments NVRAM, in accordance with an embodiment.

FIG. 19 illustrates physical memory and virtual units of memory, withmapping, a device layer and a logical layer, in an embodiment of astorage system.

FIG. 20 illustrates the defragmenter of FIG. 18 relocating data in theNVRAM, to free up contiguous space in the NVRAM, in accordance with anembodiment.

FIG. 21 is a flow diagram of a method of operating a storage system withvirtual units of memory, which can be practiced on or by embodimentsdescribed herein and further storage system embodiments.

DETAILED DESCRIPTION

Storage systems, and further systems that have and use storage memory,benefit from various optimizations in the use of spools for transferringdata and metadata to storage memory. The mechanisms and processesdescribed herein for spooling and memory space management of spoolregion memory and storage memory, and optimizations thereof, can be usedin various combinations in various embodiments, to tune systems andstorage systems for improved throughput, latency, and efficient use ofmultiple types of memory space and communication bandwidth.

Various storage systems described herein, and further storage systems,can be optimized for distribution of selected data, according to variouscriteria, in flash or other solid-state memory. The embodiments belowprovide for an upgradeable flash/solid state drive storage system. Uponan upgrade to the system, the data from a storage shelf may be requiredto be migrated to another shelf. The embodiments provide for a systemand method that considers the space or storage capacity in the systemand the ability to adaptively and/or dynamically adjust betweendiffering migration techniques based on the monitoring of the space orstorage capacity of the system. Principles of operation, variations, andimplementation details for the adaptive migration of data for a rebuildoperation or an upgrade of the system according to the available spacein the system are provided below, with reference to FIGS. 1-10.Principles of operation, variations, and implementation details forspools, spool insertion optimizing and storage memory partitioning areprovided below, with reference to FIGS. 11-17. Storage systemembodiments that defragment NVRAM are described with reference to FIGS.18-21.

Example methods, apparatus, and products for a memory controllerscheduling system n accordance with embodiments of the presentdisclosure are described with reference to the accompanying drawings,beginning with FIG. 1A. FIG. 1A illustrates an example system for datastorage, in accordance with some implementations. System 100 (alsoreferred to as “storage system” herein) includes numerous elements forpurposes of illustration rather than limitation. It may be noted thatsystem 100 may include the same, more, or fewer elements configured inthe same or different manner in other implementations.

System 100 includes a number of computing devices 164A-B. Computingdevices (also referred to as “client devices” herein) may be embodied,for example, a server in a data center, a workstation, a personalcomputer, a notebook, or the like. Computing devices 164A-B may becoupled for data communications to one or more storage arrays 102A-Bthrough a storage area network (‘SAN’) 158 or a local area network(‘LAN’) 160.

The SAN 158 may be implemented with a variety of data communicationsfabrics, devices, and protocols. For example, the fabrics for SAN 158may include Fibre Channel, Ethernet, Infiniband, Serial Attached SmallComputer System Interface (‘SAS’), or the like. Data communicationsprotocols for use with SAN 158 may include Advanced TechnologyAttachment (‘ATA’), Fibre Channel Protocol, Small Computer SystemInterface (‘SCSI’), Internet Small Computer System Interface (‘iSCSI’),HyperSCSI, Non-Volatile Memory Express (‘NVMe’) over Fabrics, or thelike. It may be noted that SAN 158 is provided for illustration, ratherthan limitation. Other data communication couplings may be implementedbetween computing devices 164A-B and storage arrays 102A-B.

The LAN 160 may also be implemented with a variety of fabrics, devices,and protocols. For example, the fabrics for LAN 160 may include Ethernet(802.3), wireless (802.11), or the like. Data communication protocolsfor use in LAN 160 may include Transmission Control Protocol (‘TCP’),User Datagram Protocol (‘UDP’), Internet Protocol (‘IP’), HyperTextTransfer Protocol (‘HTTP’), Wireless Access Protocol (‘WAP’), HandheldDevice Transport Protocol (‘HDTP’), Session Initiation Protocol (‘SIP’),Real Time Protocol (‘RTP’), or the like.

Storage arrays 102A-B may provide persistent data storage for thecomputing devices 164A-B. Storage array 102A may be contained in achassis (not shown), and storage array 102B may be contained in anotherchassis (not shown), in some implementations. Storage array 102A and102B may include one or more storage array controllers 110A-D (alsoreferred to as “controller” herein). A storage array controller 110A-Dmay be embodied as a module of automated computing machinery comprisingcomputer hardware, computer software, or a combination of computerhardware and software. In some implementations, the storage arraycontrollers 110A-D may be configured to carry out various storage tasks.Storage tasks may include writing data received from the computingdevices 164A-B to storage array 102A-B, erasing data from storage array102A-B, retrieving data from storage array 102A-B and providing data tocomputing devices 164A-B, monitoring and reporting of storage deviceutilization and performance, performing redundancy operations, such asRedundant Array of Independent Drives (‘RAID’) or RAID-like dataredundancy operations, compressing data, encrypting data, and so forth.

Storage array controller 110A-D may be implemented in a variety of ways,including as a Field Programmable Gate Array (‘FPGA’), a ProgrammableLogic Chip (‘PLC’), an Application Specific Integrated Circuit (‘ASIC’),System-on-Chip (‘SOC’), or any computing device that includes discretecomponents such as a processing device, central processing unit,computer memory, or various adapters. Storage array controller 110A-Dmay include, for example, a data communications adapter configured tosupport communications via the SAN 158 or LAN 160. In someimplementations, storage array controller 110A-D may be independentlycoupled to the LAN 160. In some implementations, storage arraycontroller 110A-D may include an I/O controller or the like that couplesthe storage array controller 110A-D for data communications, through amidplane (not shown), to a persistent storage resource 170A-B (alsoreferred to as a “storage resource” herein). The persistent storageresource 170A-B may include any number of storage drives 171A-F (alsoreferred to as “storage devices” herein) and any number of non-volatileRandom Access Memory (‘NVRAM’) devices (not shown).

In some implementations, the NVRAM devices of a persistent storageresource 170A-B may be configured to receive, from the storage arraycontroller 110A-D, data to be stored in the storage drives 171A-F. Insome examples, the data may originate from computing devices 164A-B. Insome examples, writing data to the NVRAM device may be carried out morequickly than directly writing data to the storage drive 171A-F. In someimplementations, the storage array controller 110A-D may be configuredto utilize the NVRAM devices as a quickly accessible buffer for datadestined to be written to the storage drives 171A-F. Latency for writerequests using NVRAM devices as a buffer may be improved relative to asystem in which a storage array controller 110A-D writes data directlyto the storage drives 171A-F. In some implementations, the NVRAM devicesmay be implemented with computer memory in the form of high bandwidth,low latency RAM. The NVRAM device is referred to as “non-volatile”because the NVRAM device may receive or include a unique power sourcethat maintains the state of the RAM after main power loss to the NVRAMdevice. Such a power source may be a battery, one or more capacitors, orthe like. In response to a power loss, the NVRAM device may beconfigured to write the contents of the RAM to a persistent storage,such as the storage drives 171A-F.

In some implementations, storage drive 171A-F may refer to any deviceconfigured to record data persistently, where “persistently” or“persistent” refers as to a device's ability to maintain recorded dataafter loss of power. In some implementations, storage drive 171A-F maycorrespond to non-disk storage media. For example, the storage drive171A-F may be one or more solid-state drives (‘SSDs’), flash memorybased storage, any type of solid-state non-volatile memory, or any othertype of non-mechanical storage device. In other implementations, storagedrive 171A-F may include mechanical or spinning hard disk, such ashard-disk drives (‘HDD’).

In some implementations, the storage array controllers 110A-D may beconfigured for offloading device management responsibilities fromstorage drive 171A-F in storage array 102A-B. For example, storage arraycontrollers 110A-D may manage control information that may describe thestate of one or more memory blocks in the storage drives 171A-F. Thecontrol information may indicate, for example, that a particular memoryblock has failed and should no longer be written to, that a particularmemory block contains boot code for a storage array controller 110A-D,the number of program-erase (‘P/E’) cycles that have been performed on aparticular memory block, the age of data stored in a particular memoryblock, the type of data that is stored in a particular memory block, andso forth. In some implementations, the control information may be storedwith an associated memory block as metadata. In other implementations,the control information for the storage drives 171A-F may be stored inone or more particular memory blocks of the storage drives 171A-F thatare selected by the storage array controller 110A-D. The selected memoryblocks may be tagged with an identifier indicating that the selectedmemory block contains control information. The identifier may beutilized by the storage array controllers 110A-D in conjunction withstorage drives 171A-F to quickly identify the memory blocks that containcontrol information. For example, the storage controllers 110A-D mayissue a command to locate memory blocks that contain controlinformation. It may be noted that control information may be so largethat parts of the control information may be stored in multiplelocations, that the control information may be stored in multiplelocations for purposes of redundancy, for example, or that the controlinformation may otherwise be distributed across multiple memory blocksin the storage drives 171A-F.

In some implementations, storage array controllers 110A-D may offloaddevice management responsibilities from storage drives 171A-F of storagearray 102A-B by retrieving, from the storage drives 171A-F, controlinformation describing the state of one or more memory blocks in thestorage drives 171A-F. Retrieving the control information from thestorage drives 171A-F may be carried out, for example, by the storagearray controller 110A-D querying the storage drives 171A-F for thelocation of control information for a particular storage drive 171A-F.The storage drives 171A-F may be configured to execute instructions thatenable the storage drives 171A-F to identify the location of the controlinformation. The instructions may be executed by a controller (notshown) associated with or otherwise located on the storage drive 171A-Fand may cause the storage drive 171A-F to scan a portion of each memoryblock to identify the memory blocks that store control information forthe storage drives 171A-F. The storage drives 171A-F may respond bysending a response message to the storage array controller 110A-D thatincludes the location of control information for the storage drive171A-F. Responsive to receiving the response message, storage arraycontrollers 110A-D may issue a request to read data stored at theaddress associated with the location of control information for thestorage drives 171A-F.

In other implementations, the storage array controllers 110A-D mayfurther offload device management responsibilities from storage drives171A-F by performing, in response to receiving the control information,a storage drive management operation. A storage drive managementoperation may include, for example, an operation that is typicallyperformed by the storage drive 171A-F (e.g., the controller (not shown)associated with a particular storage drive 171A-F). A storage drivemanagement operation may include, for example, ensuring that data is notwritten to failed memory blocks within the storage drive 171A-F,ensuring that data is written to memory blocks within the storage drive171A-F in such a way that adequate wear leveling is achieved, and soforth.

In some implementations, storage array 102A-B may implement two or morestorage array controllers 110A-D. For example, storage array 102A mayinclude storage array controllers 110A and storage array controllers110B. At a given instant, a single storage array controller 110A-D(e.g., storage array controller 110A) of a storage system 100 may bedesignated with primary status (also referred to as “primary controller”herein), and other storage array controllers 110A-D (e.g., storage arraycontroller 110A) may be designated with secondary status (also referredto as “secondary controller” herein). The primary controller may haveparticular rights, such as permission to alter data in persistentstorage resource 170A-B (e.g., writing data to persistent storageresource 170A-B). At least some of the rights of the primary controllermay supersede the rights of the secondary controller. For instance, thesecondary controller may not have permission to alter data in persistentstorage resource 170A-B when the primary controller has the right. Thestatus of storage array controllers 110A-D may change. For example,storage array controller 110A may be designated with secondary status,and storage array controller 110B may be designated with primary status.

In some implementations, a primary controller, such as storage arraycontroller 110A, may serve as the primary controller for one or morestorage arrays 102A-B, and a second controller, such as storage arraycontroller 110B, may serve as the secondary controller for the one ormore storage arrays 102A-B. For example, storage array controller 110Amay be the primary controller for storage array 102A and storage array102B, and storage array controller 110B may be the secondary controllerfor storage array 102A and 102B. In some implementations, storage arraycontrollers 110C and 110D (also referred to as “storage processingmodules”) may neither have primary or secondary status. Storage arraycontrollers 110C and 110D, implemented as storage processing modules,may act as a communication interface between the primary and secondarycontrollers (e.g., storage array controllers 110A and 110B,respectively) and storage array 102B. For example, storage arraycontroller 110A of storage array 102A may send a write request, via SAN158, to storage array 102B. The write request may be received by bothstorage array controllers 110C and 110D of storage array 102B. Storagearray controllers 110C and 110D facilitate the communication, e.g., sendthe write request to the appropriate storage drive 171A-F. It may benoted that in some implementations storage processing modules may beused to increase the number of storage drives controlled by the primaryand secondary controllers.

In some implementations, storage array controllers 110A-D arecommunicatively coupled, via a midplane (not shown), to one or morestorage drives 171A-F and to one or more NVRAM devices (not shown) thatare included as part of a storage array 102A-B. The storage arraycontrollers 110A-D may be coupled to the midplane via one or more datacommunication links and the midplane may be coupled to the storagedrives 171A-F and the NVRAM devices via one or more data communicationslinks. The data communications links described herein are collectivelyillustrated by data communications links 108A-D and may include aPeripheral Component Interconnect Express (‘PCIe’) bus, for example.

FIG. 1B illustrates an example system for data storage, in accordancewith some implementations. Storage array controller 101 illustrated inFIG. 1B may be similar to the storage array controllers 110A-D describedwith respect to FIG. 1A. In one example, storage array controller 101may be similar to storage array controller 110A or storage arraycontroller 110B. Storage array controller 101 includes numerous elementsfor purposes of illustration rather than limitation. It may be notedthat storage array controller 101 may include the same, more, or fewerelements configured in the same or different manner in otherimplementations. It may be noted that elements of FIG. 1A may beincluded below to help illustrate features of storage array controller101.

Storage array controller 101 may include one or more processing devices104 and random access memory (‘RAM’) 111. Processing device 104 (orcontroller 101) represents one or more general-purpose processingdevices such as a microprocessor, central processing unit, or the like.More particularly, the processing device 104 (or controller 101) may bea complex instruction set computing (‘CISC’) microprocessor, reducedinstruction set computing (‘RISC’) microprocessor, very long instructionword (‘VLIW’) microprocessor, or a processor implementing otherinstruction sets or processors implementing a combination of instructionsets. The processing device 104 (or controller 101) may also be one ormore special-purpose processing devices such as an ASIC, an FPGA, adigital signal processor (‘DSP’), network processor, or the like.

The processing device 104 may be connected to the RAM 111 via a datacommunications link 106, which may be embodied as a high speed memorybus such as a Double-Data Rate 4 (‘DDR4’) bus. Stored in RAM 111 is anoperating system 112. In some implementations, instructions 113 arestored in RAM 111. Instructions 113 may include computer programinstructions for performing operations in in a direct-mapped flashstorage system. In one embodiment, a direct-mapped flash storage systemis one that that addresses data blocks within flash drives directly andwithout an address translation performed by the storage controllers ofthe flash drives.

In some implementations, storage array controller 101 includes one ormore host bus adapters 103A-C that are coupled to the processing device104 via a data communications link 105A-C. In some implementations, hostbus adapters 103A-C may be computer hardware that connects a host system(e.g., the storage array controller) to other network and storagearrays. In some examples, host bus adapters 103A-C may be a FibreChannel adapter that enables the storage array controller 101 to connectto a SAN, an Ethernet adapter that enables the storage array controller101 to connect to a LAN, or the like. Host bus adapters 103A-C may becoupled to the processing device 104 via a data communications link105A-C such as, for example, a PCIe bus.

In some implementations, storage array controller 101 may include a hostbus adapter 114 that is coupled to an expander 115. The expander 115 maybe used to attach a host system to a larger number of storage drives.The expander 115 may, for example, be a SAS expander utilized to enablethe host bus adapter 114 to attach to storage drives in animplementation where the host bus adapter 114 is embodied as a SAScontroller.

In some implementations, storage array controller 101 may include aswitch 116 coupled to the processing device 104 via a datacommunications link 109. The switch 116 may be a computer hardwaredevice that can create multiple endpoints out of a single endpoint,thereby enabling multiple devices to share a single endpoint. The switch116 may, for example, be a PCIe switch that is coupled to a PCIe bus(e.g., data communications link 109) and presents multiple PCIeconnection points to the midplane.

In some implementations, storage array controller 101 includes a datacommunications link 107 for coupling the storage array controller 101 toother storage array controllers. In some examples, data communicationslink 107 may be a QuickPath Interconnect (QPI) interconnect.

A traditional storage system that uses traditional flash drives mayimplement a process across the flash drives that are part of thetraditional storage system. For example, a higher level process of thestorage system may initiate and control a process across the flashdrives. However, a flash drive of the traditional storage system mayinclude its own storage controller that also performs the process. Thus,for the traditional storage system, a higher level process (e.g.,initiated by the storage system) and a lower level process (e.g.,initiated by a storage controller of the storage system) may both beperformed.

To resolve various deficiencies of a traditional storage system,operations may be performed by higher level processes and not by thelower level processes. For example, the flash storage system may includeflash drives that do not include storage controllers that provide theprocess. Thus, the operating system of the flash storage system itselfmay initiate and control the process. This may be accomplished by adirect-mapped flash storage system that addresses data blocks within theflash drives directly and without an address translation performed bythe storage controllers of the flash drives.

In some implementations, storage drive 171A-F may be one or more zonedstorage devices. In some implementations, the one or more zoned storagedevices may be a shingled HDD. In some implementations, the one or morestorage devices may be a flash-based SSD. In a zoned storage device, azoned namespace on the zoned storage device can be addressed by groupsof blocks that are grouped and aligned by a natural size, forming anumber of addressable zones. In some implementations utilizing an SSD,the natural size may be based on the erase block size of the SSD. Insome implementations, the zones of the zoned storage device may bedefined during initialization of the zoned storage device. In someimplementations, the zones may be defined dynamically as data is writtento the zoned storage device.

In some implementations, zones may be heterogeneous, with some zoneseach being a page group and other zones being multiple page groups. Insome implementations, some zones may correspond to an erase block andother zones may correspond to multiple erase blocks. In animplementation, zones may be any combination of differing numbers ofpages in page groups and/or erase blocks, for heterogeneous mixes ofprogramming modes, manufacturers, product types and/or productgenerations of storage devices, as applied to heterogeneous assemblies,upgrades, distributed storages, etc. In some implementations, zones maybe defined as having usage characteristics, such as a property ofsupporting data with particular kinds of longevity (very short lived orvery long lived, for example). These properties could be used by a zonedstorage device to determine how the zone will be managed over the zone'sexpected lifetime.

It should be appreciated that a zone is a virtual construct. Anyparticular zone may not have a fixed location at a storage device. Untilallocated, a zone may not have any location at a storage device. A zonemay correspond to a number representing a chunk of virtually allocatablespace that is the size of an erase block or other block size in variousimplementations. When the system allocates or opens a zone, zones getallocated to flash or other solid-state storage memory and, as thesystem writes to the zone, pages are written to that mapped flash orother solid-state storage memory of the zoned storage device. When thesystem closes the zone, the associated erase block(s) or other sizedblock(s) are completed. At some point in the future, the system maydelete a zone which will free up the zone's allocated space. During itslifetime, a zone may be moved around to different locations of the zonedstorage device, e.g., as the zoned storage device does internalmaintenance.

In some implementations, the zones of the zoned storage device may be indifferent states. A zone may be in an empty state in which data has notbeen stored at the zone. An empty zone may be opened explicitly, orimplicitly by writing data to the zone. This is the initial state forzones on a fresh zoned storage device, but may also be the result of azone reset. In some implementations, an empty zone may have a designatedlocation within the flash memory of the zoned storage device. In animplementation, the location of the empty zone may be chosen when thezone is first opened or first written to (or later if writes arebuffered into memory). A zone may be in an open state either implicitlyor explicitly, where a zone that is in an open state may be written tostore data with write or append commands. In an implementation, a zonethat is in an open state may also be written to using a copy commandthat copies data from a different zone. In some implementations, a zonedstorage device may have a limit on the number of open zones at aparticular time.

A zone in a closed state is a zone that has been partially written to,but has entered a closed state after issuing an explicit closeoperation. A zone in a closed state may be left available for futurewrites, but may reduce some of the run-time overhead consumed by keepingthe zone in an open state. In some implementations, a zoned storagedevice may have a limit on the number of closed zones at a particulartime. A zone in a full state is a zone that is storing data and can nolonger be written to. A zone may be in a full state either after writeshave written data to the entirety of the zone or as a result of a zonefinish operation. Prior to a finish operation, a zone may or may nothave been completely written. After a finish operation, however, thezone may not be opened a written to further without first performing azone reset operation.

The mapping from a zone to an erase block (or to a shingled track in anHDD) may be arbitrary, dynamic, and hidden from view. The process ofopening a zone may be an operation that allows a new zone to bedynamically mapped to underlying storage of the zoned storage device,and then allows data to be written through appending writes into thezone until the zone reaches capacity. The zone can be finished at anypoint, after which further data may not be written into the zone. Whenthe data stored at the zone is no longer needed, the zone can be resetwhich effectively deletes the zone's content from the zoned storagedevice, making the physical storage held by that zone available for thesubsequent storage of data. Once a zone has been written and finished,the zoned storage device ensures that the data stored at the zone is notlost until the zone is reset. In the time between writing the data tothe zone and the resetting of the zone, the zone may be moved aroundbetween shingle tracks or erase blocks as part of maintenance operationswithin the zoned storage device, such as by copying data to keep thedata refreshed or to handle memory cell aging in an SSD.

In some implementations utilizing an HDD, the resetting of the zone mayallow the shingle tracks to be allocated to a new, opened zone that maybe opened at some point in the future. In some implementations utilizingan SSD, the resetting of the zone may cause the associated physicalerase block(s) of the zone to be erased and subsequently reused for thestorage of data. In some implementations, the zoned storage device mayhave a limit on the number of open zones at a point in time to reducethe amount of overhead dedicated to keeping zones open.

The operating system of the flash storage system may identify andmaintain a list of allocation units across multiple flash drives of theflash storage system. The allocation units may be entire erase blocks ormultiple erase blocks. The operating system may maintain a map oraddress range that directly maps addresses to erase blocks of the flashdrives of the flash storage system.

Direct mapping to the erase blocks of the flash drives may be used torewrite data and erase data. For example, the operations may beperformed on one or more allocation units that include a first data anda second data where the first data is to be retained and the second datais no longer being used by the flash storage system. The operatingsystem may initiate the process to write the first data to new locationswithin other allocation units and erasing the second data and markingthe allocation units as being available for use for subsequent data.Thus, the process may only be performed by the higher level operatingsystem of the flash storage system without an additional lower levelprocess being performed by controllers of the flash drives.

Advantages of the process being performed only by the operating systemof the flash storage system include increased reliability of the flashdrives of the flash storage system as unnecessary or redundant writeoperations are not being performed during the process. One possiblepoint of novelty here is the concept of initiating and controlling theprocess at the operating system of the flash storage system. Inaddition, the process can be controlled by the operating system acrossmultiple flash drives. This is contrast to the process being performedby a storage controller of a flash drive.

A storage system can consist of two storage array controllers that sharea set of drives for failover purposes, or it could consist of a singlestorage array controller that provides a storage service that utilizesmultiple drives, or it could consist of a distributed network of storagearray controllers each with some number of drives or some amount ofFlash storage where the storage array controllers in the networkcollaborate to provide a complete storage service and collaborate onvarious aspects of a storage service including storage allocation andgarbage collection.

FIG. 1C illustrates a third example system 117 for data storage inaccordance with some implementations. System 117 (also referred to as“storage system” herein) includes numerous elements for purposes ofillustration rather than limitation. It may be noted that system 117 mayinclude the same, more, or fewer elements configured in the same ordifferent manner in other implementations.

In one embodiment, system 117 includes a dual Peripheral ComponentInterconnect (‘PCI’) flash storage device 118 with separatelyaddressable fast write storage. System 117 may include a storage devicecontroller 119. In one embodiment, storage device controller 119A-D maybe a CPU, ASIC, FPGA, or any other circuitry that may implement controlstructures necessary according to the present disclosure. In oneembodiment, system 117 includes flash memory devices (e.g., includingflash memory devices 120 a-n), operatively coupled to various channelsof the storage device controller 119. Flash memory devices 120 a-n, maybe presented to the controller 119A-D as an addressable collection ofFlash pages, erase blocks, and/or control elements sufficient to allowthe storage device controller 119A-D to program and retrieve variousaspects of the Flash. In one embodiment, storage device controller119A-D may perform operations on flash memory devices 120 a-n includingstoring and retrieving data content of pages, arranging and erasing anyblocks, tracking statistics related to the use and reuse of Flash memorypages, erase blocks, and cells, tracking and predicting error codes andfaults within the Flash memory, controlling voltage levels associatedwith programming and retrieving contents of Flash cells, etc.

In one embodiment, system 117 may include RAM 121 to store separatelyaddressable fast-write data. In one embodiment, RAM 121 may be one ormore separate discrete devices. In another embodiment, RAM 121 may beintegrated into storage device controller 119A-D or multiple storagedevice controllers. The RAM 121 may be utilized for other purposes aswell, such as temporary program memory for a processing device (e.g., aCPU) in the storage device controller 119.

In one embodiment, system 117 may include a stored energy device 122,such as a rechargeable battery or a capacitor. Stored energy device 122may store energy sufficient to power the storage device controller 119,some amount of the RAM (e.g., RAM 121), and some amount of Flash memory(e.g., Flash memory 120 a-120 n) for sufficient time to write thecontents of RAM to Flash memory. In one embodiment, storage devicecontroller 119A-D may write the contents of RAM to Flash Memory if thestorage device controller detects loss of external power.

In one embodiment, system 117 includes two data communications links 123a, 123 b. In one embodiment, data communications links 123 a, 123 b maybe PCI interfaces. In another embodiment, data communications links 123a, 123 b may be based on other communications standards (e.g.,HyperTransport, InfiniBand, etc.). Data communications links 123 a, 123b may be based on non-volatile memory express (‘NVMe’) or NVMe overfabrics (‘NVMf’) specifications that allow external connection to thestorage device controller 119A-D from other components in the storagesystem 117. It should be noted that data communications links may beinterchangeably referred to herein as PCI buses for convenience.

System 117 may also include an external power source (not shown), whichmay be provided over one or both data communications links 123 a, 123 b,or which may be provided separately. An alternative embodiment includesa separate Flash memory (not shown) dedicated for use in storing thecontent of RAM 121. The storage device controller 119A-D may present alogical device over a PCI bus which may include an addressablefast-write logical device, or a distinct part of the logical addressspace of the storage device 118, which may be presented as PCI memory oras persistent storage. In one embodiment, operations to store into thedevice are directed into the RAM 121. On power failure, the storagedevice controller 119A-D may write stored content associated with theaddressable fast-write logical storage to Flash memory (e.g., Flashmemory 120 a-n) for long-term persistent storage.

In one embodiment, the logical device may include some presentation ofsome or all of the content of the Flash memory devices 120 a-n, wherethat presentation allows a storage system including a storage device 118(e.g., storage system 117) to directly address Flash memory pages anddirectly reprogram erase blocks from storage system components that areexternal to the storage device through the PCI bus. The presentation mayalso allow one or more of the external components to control andretrieve other aspects of the Flash memory including some or all of:tracking statistics related to use and reuse of Flash memory pages,erase blocks, and cells across all the Flash memory devices; trackingand predicting error codes and faults within and across the Flash memorydevices; controlling voltage levels associated with programming andretrieving contents of Flash cells; etc.

In one embodiment, the stored energy device 122 may be sufficient toensure completion of in-progress operations to the Flash memory devices120 a-120 n stored energy device 122 may power storage device controller119A-D and associated Flash memory devices (e.g., 120 a-n) for thoseoperations, as well as for the storing of fast-write RAM to Flashmemory. Stored energy device 122 may be used to store accumulatedstatistics and other parameters kept and tracked by the Flash memorydevices 120 a-n and/or the storage device controller 119. Separatecapacitors or stored energy devices (such as smaller capacitors near orembedded within the Flash memory devices themselves) may be used forsome or all of the operations described herein.

Various schemes may be used to track and optimize the life span of thestored energy component, such as adjusting voltage levels over time,partially discharging the stored energy device 122 to measurecorresponding discharge characteristics, etc. If the available energydecreases over time, the effective available capacity of the addressablefast-write storage may be decreased to ensure that it can be writtensafely based on the currently available stored energy.

FIG. 1D illustrates a third example storage system 124 for data storagein accordance with some implementations. In one embodiment, storagesystem 124 includes storage controllers 125 a, 125 b. In one embodiment,storage controllers 125 a, 125 b are operatively coupled to Dual PCIstorage devices. Storage controllers 125 a, 125 b may be operativelycoupled (e.g., via a storage network 130) to some number of hostcomputers 127 a-n.

In one embodiment, two storage controllers (e.g., 125 a and 125 b)provide storage services, such as a SCS) block storage array, a fileserver, an object server, a database or data analytics service, etc. Thestorage controllers 125 a, 125 b may provide services through somenumber of network interfaces (e.g., 126 a-d) to host computers 127 a-noutside of the storage system 124. Storage controllers 125 a, 125 b mayprovide integrated services or an application entirely within thestorage system 124, forming a converged storage and compute system. Thestorage controllers 125 a, 125 b may utilize the fast write memorywithin or across storage devices 119 a-d to journal in progressoperations to ensure the operations are not lost on a power failure,storage controller removal, storage controller or storage systemshutdown, or some fault of one or more software or hardware componentswithin the storage system 124.

In one embodiment, storage controllers 125 a, 125 b operate as PCImasters to one or the other PCI buses 128 a, 128 b. In anotherembodiment, 128 a and 128 b may be based on other communicationsstandards (e.g., HyperTransport, InfiniBand, etc.). Other storage systemembodiments may operate storage controllers 125 a, 125 b asmulti-masters for both PCI buses 128 a, 128 b. Alternately, aPCI/NVMe/NVMf switching infrastructure or fabric may connect multiplestorage controllers. Some storage system embodiments may allow storagedevices to communicate with each other directly rather thancommunicating only with storage controllers. In one embodiment, astorage device controller 119 a may be operable under direction from astorage controller 125 a to synthesize and transfer data to be storedinto Flash memory devices from data that has been stored in RAM (e.g.,RAM 121 of FIG. 1C). For example, a recalculated version of RAM contentmay be transferred after a storage controller has determined that anoperation has fully committed across the storage system, or whenfast-write memory on the device has reached a certain used capacity, orafter a certain amount of time, to ensure improve safety of the data orto release addressable fast-write capacity for reuse. This mechanism maybe used, for example, to avoid a second transfer over a bus (e.g., 128a, 128 b) from the storage controllers 125 a, 125 b. In one embodiment,a recalculation may include compressing data, attaching indexing orother metadata, combining multiple data segments together, performingerasure code calculations, etc.

In one embodiment, under direction from a storage controller 125 a, 125b, a storage device controller 119 a, 119 b may be operable to calculateand transfer data to other storage devices from data stored in RAM(e.g., RAM 121 of FIG. 1C) without involvement of the storagecontrollers 125 a, 125 b. This operation may be used to mirror datastored in one storage controller 125 a to another storage controller 125b, or it could be used to offload compression, data aggregation, and/orerasure coding calculations and transfers to storage devices to reduceload on storage controllers or the storage controller interface 129 a,129 b to the PCI bus 128 a, 128 b.

A storage device controller 119A-D may include mechanisms forimplementing high availability primitives for use by other parts of astorage system external to the Dual PCI storage device 118. For example,reservation or exclusion primitives may be provided so that, in astorage system with two storage controllers providing a highly availablestorage service, one storage controller may prevent the other storagecontroller from accessing or continuing to access the storage device.This could be used, for example, in cases where one controller detectsthat the other controller is not functioning properly or where theinterconnect between the two storage controllers may itself not befunctioning properly.

In one embodiment, a storage system for use with Dual PCI direct mappedstorage devices with separately addressable fast write storage includessystems that manage erase blocks or groups of erase blocks as allocationunits for storing data on behalf of the storage service, or for storingmetadata (e.g., indexes, logs, etc.) associated with the storageservice, or for proper management of the storage system itself. Flashpages, which may be a few kilobytes in size, may be written as dataarrives or as the storage system is to persist data for long intervalsof time (e.g., above a defined threshold of time). To commit data morequickly, or to reduce the number of writes to the Flash memory devices,the storage controllers may first write data into the separatelyaddressable fast write storage on one more storage devices.

In one embodiment, the storage controllers 125 a, 125 b may initiate theuse of erase blocks within and across storage devices (e.g., 118) inaccordance with an age and expected remaining lifespan of the storagedevices, or based on other statistics. The storage controllers 125 a,125 b may initiate garbage collection and data migration data betweenstorage devices in accordance with pages that are no longer needed aswell as to manage Flash page and erase block lifespans and to manageoverall system performance.

In one embodiment, the storage system 124 may utilize mirroring and/orerasure coding schemes as part of storing data into addressable fastwrite storage and/or as part of writing data into allocation unitsassociated with erase blocks. Erasure codes may be used across storagedevices, as well as within erase blocks or allocation units, or withinand across Flash memory devices on a single storage device, to provideredundancy against single or multiple storage device failures or toprotect against internal corruptions of Flash memory pages resultingfrom Flash memory operations or from degradation of Flash memory cells.Mirroring and erasure coding at various levels may be used to recoverfrom multiple types of failures that occur separately or in combination.

The embodiments depicted with reference to FIGS. 2A-G illustrate astorage cluster that stores user data, such as user data originatingfrom one or more user or client systems or other sources external to thestorage cluster. The storage cluster distributes user data acrossstorage nodes housed within a chassis, or across multiple chassis, usingerasure coding and redundant copies of metadata. Erasure coding refersto a method of data protection or reconstruction in which data is storedacross a set of different locations, such as disks, storage nodes orgeographic locations. Flash memory is one type of solid-state memorythat may be integrated with the embodiments, although the embodimentsmay be extended to other types of solid-state memory or other storagemedium, including non-solid state memory. Control of storage locationsand workloads are distributed across the storage locations in aclustered peer-to-peer system. Tasks such as mediating communicationsbetween the various storage nodes, detecting when a storage node hasbecome unavailable, and balancing I/Os (inputs and outputs) across thevarious storage nodes, are all handled on a distributed basis. Data islaid out or distributed across multiple storage nodes in data fragmentsor stripes that support data recovery in some embodiments. Ownership ofdata can be reassigned within a cluster, independent of input and outputpatterns. This architecture described in more detail below allows astorage node in the cluster to fail, with the system remainingoperational, since the data can be reconstructed from other storagenodes and thus remain available for input and output operations. Invarious embodiments, a storage node may be referred to as a clusternode, a blade, or a server.

The storage cluster may be contained within a chassis, i.e., anenclosure housing one or more storage nodes. A mechanism to providepower to each storage node, such as a power distribution bus, and acommunication mechanism, such as a communication bus that enablescommunication between the storage nodes are included within the chassis.The storage cluster can run as an independent system in one locationaccording to some embodiments. In one embodiment, a chassis contains atleast two instances of both the power distribution and the communicationbus which may be enabled or disabled independently. The internalcommunication bus may be an Ethernet bus, however, other technologiessuch as PCIe, InfiniBand, and others, are equally suitable. The chassisprovides a port for an external communication bus for enablingcommunication between multiple chassis, directly or through a switch,and with client systems. The external communication may use a technologysuch as Ethernet, InfiniBand, Fibre Channel, etc. In some embodiments,the external communication bus uses different communication bustechnologies for inter-chassis and client communication. If a switch isdeployed within or between chassis, the switch may act as a translationbetween multiple protocols or technologies. When multiple chassis areconnected to define a storage cluster, the storage cluster may beaccessed by a client using either proprietary interfaces or standardinterfaces such as network file system (‘NFS’), common interne filesystem (‘CIFS’), small computer system interface (‘SCSI’) or hypertexttransfer protocol (‘HTTP’). Translation from the client protocol mayoccur at the switch, chassis external communication bus or within eachstorage node. In some embodiments, multiple chassis may be coupled orconnected to each other through an aggregator switch. A portion and/orall of the coupled or connected chassis may be designated as a storagecluster. As discussed above, each chassis can have multiple blades, eachblade has a media access control (‘MAC’) address, but the storagecluster is presented to an external network as having a single clusterIP address and a single MAC address in some embodiments.

Each storage node may be one or more storage servers and each storageserver is connected to one or more non-volatile solid state memoryunits, which may be referred to as storage units or storage devices. Oneembodiment includes a single storage server in each storage node andbetween one to eight non-volatile solid state memory units, however thisone example is not meant to be limiting. The storage server may includea processor, DRAM and interfaces for the internal communication bus andpower distribution for each of the power buses. Inside the storage node,the interfaces and storage unit share a communication bus, e.g., PCIExpress, in some embodiments. The non-volatile solid state memory unitsmay directly access the internal communication bus interface through astorage node communication bus, or request the storage node to accessthe bus interface. The non-volatile solid state memory unit contains anembedded CPU, solid state storage controller, and a quantity of solidstate mass storage, e.g., between 2-32 terabytes (‘TB’) in someembodiments. An embedded volatile storage medium, such as DRAM, and anenergy reserve apparatus are included in the non-volatile solid statememory unit. In some embodiments, the energy reserve apparatus is acapacitor, super-capacitor, or battery that enables transferring asubset of DRAM contents to a stable storage medium in the case of powerloss. In some embodiments, the non-volatile solid state memory unit isconstructed with a storage class memory, such as phase change ormagnetoresistive random access memory (‘MRAM’) that substitutes for DRAMand enables a reduced power hold-up apparatus.

One of many features of the storage nodes and non-volatile solid statestorage is the ability to proactively rebuild data in a storage cluster.The storage nodes and non-volatile solid state storage can determinewhen a storage node or non-volatile solid state storage in the storagecluster is unreachable, independent of whether there is an attempt toread data involving that storage node or non-volatile solid statestorage. The storage nodes and non-volatile solid state storage thencooperate to recover and rebuild the data in at least partially newlocations. This constitutes a proactive rebuild, in that the systemrebuilds data without waiting until the data is needed for a read accessinitiated from a client system employing the storage cluster. These andfurther details of the storage memory and operation thereof arediscussed below.

FIG. 2A is a perspective view of a storage cluster 161, with multiplestorage nodes 150 and internal solid-state memory coupled to eachstorage node to provide network attached storage or storage areanetwork, in accordance with some embodiments. A network attachedstorage, storage area network, or a storage cluster, or other storagememory, could include one or more storage clusters 161, each having oneor more storage nodes 150, in a flexible and reconfigurable arrangementof both the physical components and the amount of storage memoryprovided thereby. The storage cluster 161 is designed to fit in a rack,and one or more racks can be set up and populated as desired for thestorage memory. The storage cluster 161 has a chassis 138 havingmultiple slots 142. It should be appreciated that chassis 138 may bereferred to as a housing, enclosure, or rack unit. In one embodiment,the chassis 138 has fourteen slots 142, although other numbers of slotsare readily devised. For example, some embodiments have four slots,eight slots, sixteen slots, thirty-two slots, or other suitable numberof slots. Each slot 142 can accommodate one storage node 150 in someembodiments. Chassis 138 includes flaps 148 that can be utilized tomount the chassis 138 on a rack. Fans 144 provide air circulation forcooling of the storage nodes 150 and components thereof, although othercooling components could be used, or an embodiment could be devisedwithout cooling components. A switch fabric 146 couples storage nodes150 within chassis 138 together and to a network for communication tothe memory. In an embodiment depicted in herein, the slots 142 to theleft of the switch fabric 146 and fans 144 are shown occupied by storagenodes 150, while the slots 142 to the right of the switch fabric 146 andfans 144 are empty and available for insertion of storage node 150 forillustrative purposes. This configuration is one example, and one ormore storage nodes 150 could occupy the slots 142 in various furtherarrangements. The storage node arrangements need not be sequential oradjacent in some embodiments. Storage nodes 150 are hot pluggable,meaning that a storage node 150 can be inserted into a slot 142 in thechassis 138, or removed from a slot 142, without stopping or poweringdown the system. Upon insertion or removal of storage node 150 from slot142, the system automatically reconfigures in order to recognize andadapt to the change. Reconfiguration, in some embodiments, includesrestoring redundancy and/or rebalancing data or load.

Each storage node 150 can have multiple components. In the embodimentshown here, the storage node 150 includes a printed circuit board 159populated by a CPU 156, i.e., processor, a memory 154 coupled to the CPU156, and a non-volatile solid state storage 152 coupled to the CPU 156,although other mountings and/or components could be used in furtherembodiments. The memory 154 has instructions which are executed by theCPU 156 and/or data operated on by the CPU 156. As further explainedbelow, the non-volatile solid state storage 152 includes flash or, infurther embodiments, other types of solid-state memory.

Referring to FIG. 2A, storage cluster 161 is scalable, meaning thatstorage capacity with non-uniform storage sizes is readily added, asdescribed above. One or more storage nodes 150 can be plugged into orremoved from each chassis and the storage cluster self-configures insome embodiments. Plug-in storage nodes 150, whether installed in achassis as delivered or later added, can have different sizes. Forexample, in one embodiment a storage node 150 can have any multiple of 4TB, e.g., 8 TB, 12 TB, 16 TB, 32 TB, etc. In further embodiments, astorage node 150 could have any multiple of other storage amounts orcapacities. Storage capacity of each storage node 150 is broadcast, andinfluences decisions of how to stripe the data. For maximum storageefficiency, an embodiment can self-configure as wide as possible in thestripe, subject to a predetermined requirement of continued operationwith loss of up to one, or up to two, non-volatile solid state storage152 units or storage nodes 150 within the chassis.

FIG. 2B is a block diagram showing a communications interconnect 173 andpower distribution bus 172 coupling multiple storage nodes 150.Referring back to FIG. 2A, the communications interconnect 173 can beincluded in or implemented with the switch fabric 146 in someembodiments. Where multiple storage clusters 161 occupy a rack, thecommunications interconnect 173 can be included in or implemented with atop of rack switch, in some embodiments. As illustrated in FIG. 2B,storage cluster 161 is enclosed within a single chassis 138. Externalport 176 is coupled to storage nodes 150 through communicationsinterconnect 173, while external port 174 is coupled directly to astorage node. External power port 178 is coupled to power distributionbus 172. Storage nodes 150 may include varying amounts and differingcapacities of non-volatile solid state storage 152 as described withreference to FIG. 2A. In addition, one or more storage nodes 150 may bea compute only storage node as illustrated in FIG. 2B. Authorities 168are implemented on the non-volatile solid state storage 152, for exampleas lists or other data structures stored in memory. In some embodimentsthe authorities are stored within the non-volatile solid state storage152 and supported by software executing on a controller or otherprocessor of the non-volatile solid state storage 152. In a furtherembodiment, authorities 168 are implemented on the storage nodes 150,for example as lists or other data structures stored in the memory 154and supported by software executing on the CPU 156 of the storage node150. Authorities 168 control how and where data is stored in thenon-volatile solid state storage 152 in some embodiments. This controlassists in determining which type of erasure coding scheme is applied tothe data, and which storage nodes 150 have which portions of the data.Each authority 168 may be assigned to a non-volatile solid state storage152. Each authority may control a range of inode numbers, segmentnumbers, or other data identifiers which are assigned to data by a filesystem, by the storage nodes 150, or by the non-volatile solid statestorage 152, in various embodiments.

Every piece of data, and every piece of metadata, has redundancy in thesystem in some embodiments. In addition, every piece of data and everypiece of metadata has an owner, which may be referred to as anauthority. If that authority is unreachable, for example through failureof a storage node, there is a plan of succession for how to find thatdata or that metadata. In various embodiments, there are redundantcopies of authorities 168. Authorities 168 have a relationship tostorage nodes 150 and non-volatile solid state storage 152 in someembodiments. Each authority 168, covering a range of data segmentnumbers or other identifiers of the data, may be assigned to a specificnon-volatile solid state storage 152. In some embodiments theauthorities 168 for all of such ranges are distributed over thenon-volatile solid state storage 152 of a storage cluster. Each storagenode 150 has a network port that provides access to the non-volatilesolid state storage(s) 152 of that storage node 150. Data can be storedin a segment, which is associated with a segment number and that segmentnumber is an indirection for a configuration of a RAID (redundant arrayof independent disks) stripe in some embodiments. The assignment and useof the authorities 168 thus establishes an indirection to data.Indirection may be referred to as the ability to reference dataindirectly, in this case via an authority 168, in accordance with someembodiments. A segment identifies a set of non-volatile solid statestorage 152 and a local identifier into the set of non-volatile solidstate storage 152 that may contain data. In some embodiments, the localidentifier is an offset into the device and may be reused sequentiallyby multiple segments. In other embodiments the local identifier isunique for a specific segment and never reused. The offsets in thenon-volatile solid state storage 152 are applied to locating data forwriting to or reading from the non-volatile solid state storage 152 (inthe form of a RAID stripe). Data is striped across multiple units ofnon-volatile solid state storage 152, which may include or be differentfrom the non-volatile solid state storage 152 having the authority 168for a particular data segment.

If there is a change in where a particular segment of data is located,e.g., during a data move or a data reconstruction, the authority 168 forthat data segment should be consulted, at that non-volatile solid statestorage 152 or storage node 150 having that authority 168. In order tolocate a particular piece of data, embodiments calculate a hash valuefor a data segment or apply an inode number or a data segment number.The output of this operation points to a non-volatile solid statestorage 152 having the authority 168 for that particular piece of data.In some embodiments there are two stages to this operation. The firststage maps an entity identifier (ID), e.g., a segment number, inodenumber, or directory number to an authority identifier. This mapping mayinclude a calculation such as a hash or a bit mask. The second stage ismapping the authority identifier to a particular non-volatile solidstate storage 152, which may be done through an explicit mapping. Theoperation is repeatable, so that when the calculation is performed, theresult of the calculation repeatably and reliably points to a particularnon-volatile solid state storage 152 having that authority 168. Theoperation may include the set of reachable storage nodes as input. Ifthe set of reachable non-volatile solid state storage units changes theoptimal set changes. In some embodiments, the persisted value is thecurrent assignment (which is always true) and the calculated value isthe target assignment the cluster will attempt to reconfigure towards.This calculation may be used to determine the optimal non-volatile solidstate storage 152 for an authority in the presence of a set ofnon-volatile solid state storage 152 that are reachable and constitutethe same cluster. The calculation also determines an ordered set of peernon-volatile solid state storage 152 that will also record the authorityto non-volatile solid state storage mapping so that the authority may bedetermined even if the assigned non-volatile solid state storage isunreachable. A duplicate or substitute authority 168 may be consulted ifa specific authority 168 is unavailable in some embodiments.

With reference to FIGS. 2A and 2B, two of the many tasks of the CPU 156on a storage node 150 are to break up write data, and reassemble readdata. When the system has determined that data is to be written, theauthority 168 for that data is located as above. When the segment ID fordata is already determined the request to write is forwarded to thenon-volatile solid state storage 152 currently determined to be the hostof the authority 168 determined from the segment. The host CPU 156 ofthe storage node 150, on which the non-volatile solid state storage 152and corresponding authority 168 reside, then breaks up or shards thedata and transmits the data out to various non-volatile solid statestorage 152. The transmitted data is written as a data stripe inaccordance with an erasure coding scheme. In some embodiments, data isrequested to be pulled, and in other embodiments, data is pushed. Inreverse, when data is read, the authority 168 for the segment IDcontaining the data is located as described above. The host CPU 156 ofthe storage node 150 on which the non-volatile solid state storage 152and corresponding authority 168 reside requests the data from thenon-volatile solid state storage and corresponding storage nodes pointedto by the authority. In some embodiments the data is read from flashstorage as a data stripe. The host CPU 156 of storage node 150 thenreassembles the read data, correcting any errors (if present) accordingto the appropriate erasure coding scheme, and forwards the reassembleddata to the network. In further embodiments, some or all of these taskscan be handled in the non-volatile solid state storage 152. In someembodiments, the segment host requests the data be sent to storage node150 by requesting pages from storage and then sending the data to thestorage node making the original request.

In embodiments, authorities 168 operate to determine how operations willproceed against particular logical elements. Each of the logicalelements may be operated on through a particular authority across aplurality of storage controllers of a storage system. The authorities168 may communicate with the plurality of storage controllers so thatthe plurality of storage controllers collectively perform operationsagainst those particular logical elements.

In embodiments, logical elements could be, for example, files,directories, object buckets, individual objects, delineated parts offiles or objects, other forms of key-value pair databases, or tables. Inembodiments, performing an operation can involve, for example, ensuringconsistency, structural integrity, and/or recoverability with otheroperations against the same logical element, reading metadata and dataassociated with that logical element, determining what data should bewritten durably into the storage system to persist any changes for theoperation, or where metadata and data can be determined to be storedacross modular storage devices attached to a plurality of the storagecontrollers in the storage system.

In some embodiments the operations are token based transactions toefficiently communicate within a distributed system. Each transactionmay be accompanied by or associated with a token, which gives permissionto execute the transaction. The authorities 168 are able to maintain apre-transaction state of the system until completion of the operation insome embodiments. The token based communication may be accomplishedwithout a global lock across the system, and also enables restart of anoperation in case of a disruption or other failure.

In some systems, for example in UNIX-style file systems, data is handledwith an index node or inode, which specifies a data structure thatrepresents an object in a file system. The object could be a file or adirectory, for example. Metadata may accompany the object, as attributessuch as permission data and a creation timestamp, among otherattributes. A segment number could be assigned to all or a portion ofsuch an object in a file system. In other systems, data segments arehandled with a segment number assigned elsewhere. For purposes ofdiscussion, the unit of distribution is an entity, and an entity can bea file, a directory or a segment. That is, entities are units of data ormetadata stored by a storage system. Entities are grouped into setscalled authorities. Each authority has an authority owner, which is astorage node that has the exclusive right to update the entities in theauthority. In other words, a storage node contains the authority, andthat the authority, in turn, contains entities.

A segment is a logical container of data in accordance with someembodiments. A segment is an address space between medium address spaceand physical flash locations, i.e., the data segment number, are in thisaddress space. Segments may also contain meta-data, which enable dataredundancy to be restored (rewritten to different flash locations ordevices) without the involvement of higher level software. In oneembodiment, an internal format of a segment contains client data andmedium mappings to determine the position of that data. Each datasegment is protected, e.g., from memory and other failures, by breakingthe segment into a number of data and parity shards, where applicable.The data and parity shards are distributed, i.e., striped, acrossnon-volatile solid state storage 152 coupled to the host CPUs 156 (SeeFIGS. 2E and 2G) in accordance with an erasure coding scheme. Usage ofthe term segments refers to the container and its place in the addressspace of segments in some embodiments. Usage of the term stripe refersto the same set of shards as a segment and includes how the shards aredistributed along with redundancy or parity information in accordancewith some embodiments.

A series of address-space transformations takes place across an entirestorage system. At the top are the directory entries (file names) whichlink to an inode. Inodes point into medium address space, where data islogically stored. Medium addresses may be mapped through a series ofindirect mediums to spread the load of large files, or implement dataservices like deduplication or snapshots. Medium addresses may be mappedthrough a series of indirect mediums to spread the load of large files,or implement data services like deduplication or snapshots. Segmentaddresses are then translated into physical flash locations. Physicalflash locations have an address range bounded by the amount of flash inthe system in accordance with some embodiments. Medium addresses andsegment addresses are logical containers, and in some embodiments use a128 bit or larger identifier so as to be practically infinite, with alikelihood of reuse calculated as longer than the expected life of thesystem. Addresses from logical containers are allocated in ahierarchical fashion in some embodiments. Initially, each non-volatilesolid state storage 152 unit may be assigned a range of address space.Within this assigned range, the non-volatile solid state storage 152 isable to allocate addresses without synchronization with othernon-volatile solid state storage 152.

Data and metadata is stored by a set of underlying storage layouts thatare optimized for varying workload patterns and storage devices. Theselayouts incorporate multiple redundancy schemes, compression formats andindex algorithms. Some of these layouts store information aboutauthorities and authority masters, while others store file metadata andfile data. The redundancy schemes include error correction codes thattolerate corrupted bits within a single storage device (such as a NANDflash chip), erasure codes that tolerate the failure of multiple storagenodes, and replication schemes that tolerate data center or regionalfailures. In some embodiments, low density parity check (‘LDPC’) code isused within a single storage unit. Reed-Solomon encoding is used withina storage cluster, and mirroring is used within a storage grid in someembodiments. Metadata may be stored using an ordered log structuredindex (such as a Log Structured Merge Tree), and large data may not bestored in a log structured layout.

In order to maintain consistency across multiple copies of an entity,the storage nodes agree implicitly on two things through calculations:(1) the authority that contains the entity, and (2) the storage nodethat contains the authority. The assignment of entities to authoritiescan be done by pseudo randomly assigning entities to authorities, bysplitting entities into ranges based upon an externally produced key, orby placing a single entity into each authority. Examples of pseudorandomschemes are linear hashing and the Replication Under Scalable Hashing(‘RUSH’) family of hashes, including Controlled Replication UnderScalable Hashing (‘CRUSH’). In some embodiments, pseudo-randomassignment is utilized only for assigning authorities to nodes becausethe set of nodes can change. The set of authorities cannot change so anysubjective function may be applied in these embodiments. Some placementschemes automatically place authorities on storage nodes, while otherplacement schemes rely on an explicit mapping of authorities to storagenodes. In some embodiments, a pseudorandom scheme is utilized to mapfrom each authority to a set of candidate authority owners. Apseudorandom data distribution function related to CRUSH may assignauthorities to storage nodes and create a list of where the authoritiesare assigned. Each storage node has a copy of the pseudorandom datadistribution function, and can arrive at the same calculation fordistributing, and later finding or locating an authority. Each of thepseudorandom schemes requires the reachable set of storage nodes asinput in some embodiments in order to conclude the same target nodes.Once an entity has been placed in an authority, the entity may be storedon physical devices so that no expected failure will lead to unexpecteddata loss. In some embodiments, rebalancing algorithms attempt to storethe copies of all entities within an authority in the same layout and onthe same set of machines.

Examples of expected failures include device failures, stolen machines,datacenter fires, and regional disasters, such as nuclear or geologicalevents. Different failures lead to different levels of acceptable dataloss. In some embodiments, a stolen storage node impacts neither thesecurity nor the reliability of the system, while depending on systemconfiguration, a regional event could lead to no loss of data, a fewseconds or minutes of lost updates, or even complete data loss.

In the embodiments, the placement of data for storage redundancy isindependent of the placement of authorities for data consistency. Insome embodiments, storage nodes that contain authorities do not containany persistent storage. Instead, the storage nodes are connected tonon-volatile solid state storage units that do not contain authorities.The communications interconnect between storage nodes and non-volatilesolid state storage units consists of multiple communicationtechnologies and has non-uniform performance and fault tolerancecharacteristics. In some embodiments, as mentioned above, non-volatilesolid state storage units are connected to storage nodes via PCIexpress, storage nodes are connected together within a single chassisusing Ethernet backplane, and chassis are connected together to form astorage cluster. Storage clusters are connected to clients usingEthernet or fiber channel in some embodiments. If multiple storageclusters are configured into a storage grid, the multiple storageclusters are connected using the Internet or other long-distancenetworking links, such as a “metro scale” link or private link that doesnot traverse the internet.

Authority owners have the exclusive right to modify entities, to migrateentities from one non-volatile solid state storage unit to anothernon-volatile solid state storage unit, and to add and remove copies ofentities. This allows for maintaining the redundancy of the underlyingdata. When an authority owner fails, is going to be decommissioned, oris overloaded, the authority is transferred to a new storage node.Transient failures make it non-trivial to ensure that all non-faultymachines agree upon the new authority location. The ambiguity thatarises due to transient failures can be achieved automatically by aconsensus protocol such as Paxos, hot-warm failover schemes, via manualintervention by a remote system administrator, or by a local hardwareadministrator (such as by physically removing the failed machine fromthe cluster, or pressing a button on the failed machine). In someembodiments, a consensus protocol is used, and failover is automatic. Iftoo many failures or replication events occur in too short a timeperiod, the system goes into a self-preservation mode and haltsreplication and data movement activities until an administratorintervenes in accordance with some embodiments.

As authorities are transferred between storage nodes and authorityowners update entities in their authorities, the system transfersmessages between the storage nodes and non-volatile solid state storageunits. With regard to persistent messages, messages that have differentpurposes are of different types. Depending on the type of the message,the system maintains different ordering and durability guarantees. Asthe persistent messages are being processed, the messages aretemporarily stored in multiple durable and non-durable storage hardwaretechnologies. In some embodiments, messages are stored in RAM, NVRAM andon NAND flash devices, and a variety of protocols are used in order tomake efficient use of each storage medium. Latency-sensitive clientrequests may be persisted in replicated NVRAM, and then later NAND,while background rebalancing operations are persisted directly to NAND.

Persistent messages are persistently stored prior to being transmitted.This allows the system to continue to serve client requests despitefailures and component replacement. Although many hardware componentscontain unique identifiers that are visible to system administrators,manufacturer, hardware supply chain and ongoing monitoring qualitycontrol infrastructure, applications running on top of theinfrastructure address virtualize addresses. These virtualized addressesdo not change over the lifetime of the storage system, regardless ofcomponent failures and replacements. This allows each component of thestorage system to be replaced over time without reconfiguration ordisruptions of client request processing, i.e., the system supportsnon-disruptive upgrades.

In some embodiments, the virtualized addresses are stored withsufficient redundancy. A continuous monitoring system correlateshardware and software status and the hardware identifiers. This allowsdetection and prediction of failures due to faulty components andmanufacturing details. The monitoring system also enables the proactivetransfer of authorities and entities away from impacted devices beforefailure occurs by removing the component from the critical path in someembodiments.

FIG. 2C is a multiple level block diagram, showing contents of a storagenode 150 and contents of a non-volatile solid state storage 152 of thestorage node 150. Data is communicated to and from the storage node 150by a network interface controller (‘NIC’) 202 in some embodiments. Eachstorage node 150 has a CPU 156, and one or more non-volatile solid statestorage 152, as discussed above. Moving down one level in FIG. 2C, eachnon-volatile solid state storage 152 has a relatively fast non-volatilesolid state memory, such as nonvolatile random access memory (‘NVRAM’)204, and flash memory 206. In some embodiments, NVRAM 204 may be acomponent that does not require program/erase cycles (DRAM, MRAM, PCM),and can be a memory that can support being written vastly more oftenthan the memory is read from. Moving down another level in FIG. 2C, theNVRAM 204 is implemented in one embodiment as high speed volatilememory, such as dynamic random access memory (DRAM) 216, backed up byenergy reserve 218. Energy reserve 218 provides sufficient electricalpower to keep the DRAM 216 powered long enough for contents to betransferred to the flash memory 206 in the event of power failure. Insome embodiments, energy reserve 218 is a capacitor, super-capacitor,battery, or other device, that supplies a suitable supply of energysufficient to enable the transfer of the contents of DRAM 216 to astable storage medium in the case of power loss. The flash memory 206 isimplemented as multiple flash dies 222, which may be referred to aspackages of flash dies 222 or an array of flash dies 222. It should beappreciated that the flash dies 222 could be packaged in any number ofways, with a single die per package, multiple dies per package (i.e.,multichip packages), in hybrid packages, as bare dies on a printedcircuit board or other substrate, as encapsulated dies, etc. In theembodiment shown, the non-volatile solid state storage 152 has acontroller 212 or other processor, and an input output (I/O) port 210coupled to the controller 212. I/O port 210 is coupled to the CPU 156and/or the network interface controller 202 of the flash storage node150. Flash input output (I/O) port 220 is coupled to the flash dies 222,and a direct memory access unit (DMA) 214 is coupled to the controller212, the DRAM 216 and the flash dies 222. In the embodiment shown, theI/O port 210, controller 212, DMA unit 214 and flash I/O port 220 areimplemented on a programmable logic device (‘PLD’) 208, e.g., an FPGA.In this embodiment, each flash die 222 has pages, organized as sixteenkB (kilobyte) pages 224, and a register 226 through which data can bewritten to or read from the flash die 222. In further embodiments, othertypes of solid-state memory are used in place of, or in addition toflash memory illustrated within flash die 222.

Storage clusters 161, in various embodiments as disclosed herein, can becontrasted with storage arrays in general. The storage nodes 150 arepart of a collection that creates the storage cluster 161. Each storagenode 150 owns a slice of data and computing required to provide thedata. Multiple storage nodes 150 cooperate to store and retrieve thedata. Storage memory or storage devices, as used in storage arrays ingeneral, are less involved with processing and manipulating the data.Storage memory or storage devices in a storage array receive commands toread, write, or erase data. The storage memory or storage devices in astorage array are not aware of a larger system in which they areembedded, or what the data means. Storage memory or storage devices instorage arrays can include various types of storage memory, such as RAM,solid state drives, hard disk drives, etc. The non-volatile solid statestorage 152 units described herein have multiple interfaces activesimultaneously and serving multiple purposes. In some embodiments, someof the functionality of a storage node 150 is shifted into a storageunit 152, transforming the storage unit 152 into a combination ofstorage unit 152 and storage node 150. Placing computing (relative tostorage data) into the storage unit 152 places this computing closer tothe data itself. The various system embodiments have a hierarchy ofstorage node layers with different capabilities. By contrast, in astorage array, a controller owns and knows everything about all of thedata that the controller manages in a shelf or storage devices. In astorage cluster 161, as described herein, multiple controllers inmultiple non-volatile sold state storage 152 units and/or storage nodes150 cooperate in various ways (e.g., for erasure coding, data sharding,metadata communication and redundancy, storage capacity expansion orcontraction, data recovery, and so on).

FIG. 2D shows a storage server environment, which uses embodiments ofthe storage nodes 150 and storage 152 units of FIGS. 2A-C. In thisversion, each non-volatile solid state storage 152 unit has a processorsuch as controller 212 (see FIG. 2C), an FPGA, flash memory 206, andNVRAM 204 (which is super-capacitor backed DRAM 216, see FIGS. 2B and2C) on a PCIe (peripheral component interconnect express) board in achassis 138 (see FIG. 2A). The non-volatile solid state storage 152 unitmay be implemented as a single board containing storage, and may be thelargest tolerable failure domain inside the chassis. In someembodiments, up to two non-volatile solid state storage 152 units mayfail and the device will continue with no data loss.

The physical storage is divided into named regions based on applicationusage in some embodiments. The NVRAM 204 is a contiguous block ofreserved memory in the non-volatile solid state storage 152 DRAM 216,and is backed by NAND flash. NVRAM 204 is logically divided intomultiple memory regions written for two as spool (e.g., spool_region).Space within the NVRAM 204 spools is managed by each authority 168independently. Each device provides an amount of storage space to eachauthority 168. That authority 168 further manages lifetimes andallocations within that space. Examples of a spool include distributedtransactions or notions. When the primary power to a non-volatile solidstate storage 152 unit fails, onboard super-capacitors provide a shortduration of power hold up. During this holdup interval, the contents ofthe NVRAM 204 are flushed to flash memory 206. On the next power-on, thecontents of the NVRAM 204 are recovered from the flash memory 206.

As for the storage unit controller, the responsibility of the logical“controller” is distributed across each of the blades containingauthorities 168. This distribution of logical control is shown in FIG.2D as a host controller 242, mid-tier controller 244 and storage unitcontroller(s) 246. Management of the control plane and the storage planeare treated independently, although parts may be physically co-locatedon the same blade. Each authority 168 effectively serves as anindependent controller. Each authority 168 provides its own data andmetadata structures, its own background workers, and maintains its ownlifecycle.

FIG. 2E is a blade 252 hardware block diagram, showing a control plane254, compute and storage planes 256, 258, and authorities 168interacting with underlying physical resources, using embodiments of thestorage nodes 150 and storage units 152 of FIGS. 2A-C in the storageserver environment of FIG. 2D. The control plane 254 is partitioned intoa number of authorities 168 which can use the compute resources in thecompute plane 256 to run on any of the blades 252. The storage plane 258is partitioned into a set of devices, each of which provides access toflash 206 and NVRAM 204 resources. In one embodiment, the compute plane256 may perform the operations of a storage array controller, asdescribed herein, on one or more devices of the storage plane 258 (e.g.,a storage array).

In the compute and storage planes 256, 258 of FIG. 2E, the authorities168 interact with the underlying physical resources (i.e., devices).From the point of view of an authority 168, its resources are stripedover all of the physical devices. From the point of view of a device, itprovides resources to all authorities 168, irrespective of where theauthorities happen to run. Each authority 168 has allocated or has beenallocated one or more partitions 260 of storage memory in the storageunits 152, e.g., partitions 260 in flash memory 206 and NVRAM 204. Eachauthority 168 uses those allocated partitions 260 that belong to it, forwriting or reading user data. Authorities can be associated withdiffering amounts of physical storage of the system. For example, oneauthority 168 could have a larger number of partitions 260 or largersized partitions 260 in one or more storage units 152 than one or moreother authorities 168.

FIG. 2F depicts elasticity software layers in blades 252 of a storagecluster, in accordance with some embodiments. In the elasticitystructure, elasticity software is symmetric, i.e., each blade's computemodule 270 runs the three identical layers of processes depicted in FIG.2F. Storage managers 274 execute read and write requests from otherblades 252 for data and metadata stored in local storage unit 152 NVRAM204 and flash 206. Authorities 168 fulfill client requests by issuingthe necessary reads and writes to the blades 252 on whose storage units152 the corresponding data or metadata resides. Endpoints 272 parseclient connection requests received from switch fabric 146 supervisorysoftware, relay the client connection requests to the authorities 168responsible for fulfillment, and relay the authorities' 168 responses toclients. The symmetric three-layer structure enables the storagesystem's high degree of concurrency. Elasticity scales out efficientlyand reliably in these embodiments. In addition, elasticity implements aunique scale-out technique that balances work evenly across allresources regardless of client access pattern, and maximizes concurrencyby eliminating much of the need for inter-blade coordination thattypically occurs with conventional distributed locking.

Still referring to FIG. 2F, authorities 168 running in the computemodules 270 of a blade 252 perform the internal operations required tofulfill client requests. One feature of elasticity is that authorities168 are stateless, i.e., they cache active data and metadata in theirown blades' 252 DRAMs for fast access, but the authorities store everyupdate in their NVRAM 204 partitions on three separate blades 252 untilthe update has been written to flash 206. All the storage system writesto NVRAM 204 are in triplicate to partitions on three separate blades252 in some embodiments. With triple-mirrored NVRAM 204 and persistentstorage protected by parity and Reed-Solomon RAID checksums, the storagesystem can survive concurrent failure of two blades 252 with no loss ofdata, metadata, or access to either.

Because authorities 168 are stateless, they can migrate between blades252. Each authority 168 has a unique identifier. NVRAM 204 and flash 206partitions are associated with authorities' 168 identifiers, not withthe blades 252 on which they are running in some. Thus, when anauthority 168 migrates, the authority 168 continues to manage the samestorage partitions from its new location. When a new blade 252 isinstalled in an embodiment of the storage cluster, the systemautomatically rebalances load by: partitioning the new blade's 252storage for use by the system's authorities 168, migrating selectedauthorities 168 to the new blade 252, starting endpoints 272 on the newblade 252 and including them in the switch fabric's 146 clientconnection distribution algorithm.

From their new locations, migrated authorities 168 persist the contentsof their NVRAM 204 partitions on flash 206, process read and writerequests from other authorities 168, and fulfill the client requeststhat endpoints 272 direct to them. Similarly, if a blade 252 fails or isremoved, the system redistributes its authorities 168 among the system'sremaining blades 252. The redistributed authorities 168 continue toperform their original functions from their new locations.

FIG. 2G depicts authorities 168 and storage resources in blades 252 of astorage cluster, in accordance with some embodiments. Each authority 168is exclusively responsible for a partition of the flash 206 and NVRAM204 on each blade 252. The authority 168 manages the content andintegrity of its partitions independently of other authorities 168.Authorities 168 compress incoming data and preserve it temporarily intheir NVRAM 204 partitions, and then consolidate, RAID-protect, andpersist the data in segments of the storage in their flash 206partitions. As the authorities 168 write data to flash 206, storagemanagers 274 perform the necessary flash translation to optimize writeperformance and maximize media longevity. In the background, authorities168 “garbage collect,” or reclaim space occupied by data that clientshave made obsolete by overwriting the data. It should be appreciatedthat since authorities' 168 partitions are disjoint, there is no needfor distributed locking to execute client and writes or to performbackground functions.

The embodiments described herein may utilize various software,communication and/or networking protocols. In addition, theconfiguration of the hardware and/or software may be adjusted toaccommodate various protocols. For example, the embodiments may utilizeActive Directory, which is a database based system that providesauthentication, directory, policy, and other services in a WINDOWS™environment. In these embodiments, LDAP (Lightweight Directory AccessProtocol) is one example application protocol for querying and modifyingitems in directory service providers such as Active Directory. In someembodiments, a network lock manager (‘NLM’) is utilized as a facilitythat works in cooperation with the Network File System (‘NFS’) toprovide a System V style of advisory file and record locking over anetwork. The Server Message Block (‘SMB’) protocol, one version of whichis also known as Common Internet File System (‘CIFS’), may be integratedwith the storage systems discussed herein. SMP operates as anapplication-layer network protocol typically used for providing sharedaccess to files, printers, and serial ports and miscellaneouscommunications between nodes on a network. SMB also provides anauthenticated inter-process communication mechanism. AMAZON™ S3 (SimpleStorage Service) is a web service offered by Amazon Web Services, andthe systems described herein may interface with Amazon S3 through webservices interfaces (REST (representational state transfer), SOAP(simple object access protocol), and BitTorrent). A RESTful API(application programming interface) breaks down a transaction to createa series of small modules. Each module addresses a particular underlyingpart of the transaction. The control or permissions provided with theseembodiments, especially for object data, may include utilization of anaccess control list (‘ACL’). The ACL is a list of permissions attachedto an object and the ACL specifies which users or system processes aregranted access to objects, as well as what operations are allowed ongiven objects. The systems may utilize Internet Protocol version 6(‘IPv6’), as well as IPv4, for the communications protocol that providesan identification and location system for computers on networks androutes traffic across the Internet. The routing of packets betweennetworked systems may include Equal-cost multi-path routing (‘ECMP’),which is a routing strategy where next-hop packet forwarding to a singledestination can occur over multiple “best paths” which tie for top placein routing metric calculations. Multi-path routing can be used inconjunction with most routing protocols, because it is a per-hopdecision limited to a single router. The software may supportMulti-tenancy, which is an architecture in which a single instance of asoftware application serves multiple customers. Each customer may bereferred to as a tenant. Tenants may be given the ability to customizesome parts of the application, but may not customize the application'scode, in some embodiments. The embodiments may maintain audit logs. Anaudit log is a document that records an event in a computing system. Inaddition to documenting what resources were accessed, audit log entriestypically include destination and source addresses, a timestamp, anduser login information for compliance with various regulations. Theembodiments may support various key management policies, such asencryption key rotation. In addition, the system may support dynamicroot passwords or some variation dynamically changing passwords.

FIG. 3A sets forth a diagram of a storage system 306 that is coupled fordata communications with a cloud services provider 302 in accordancewith some embodiments of the present disclosure. Although depicted inless detail, the storage system 306 depicted in FIG. 3A may be similarto the storage systems described above with reference to FIGS. 1A-1D andFIGS. 2A-2G. In some embodiments, the storage system 306 depicted inFIG. 3A may be embodied as a storage system that includes imbalancedactive/active controllers, as a storage system that includes balancedactive/active controllers, as a storage system that includesactive/active controllers where less than all of each controller'sresources are utilized such that each controller has reserve resourcesthat may be used to support failover, as a storage system that includesfully active/active controllers, as a storage system that includesdataset-segregated controllers, as a storage system that includesdual-layer architectures with front-end controllers and back-endintegrated storage controllers, as a storage system that includesscale-out clusters of dual-controller arrays, as well as combinations ofsuch embodiments.

In the example depicted in FIG. 3A, the storage system 306 is coupled tothe cloud services provider 302 via a data communications link 304. Sucha data communications link 304 may be fully wired, fully wireless, orsome aggregation of wired and wireless data communications pathways. Insuch an example, digital information may be exchanged between thestorage system 306 and the cloud services provider 302 via the datacommunications link 304 using one or more data communications protocols.For example, digital information may be exchanged between the storagesystem 306 and the cloud services provider 302 via the datacommunications link 304 using the handheld device transfer protocol(‘HDTP’), hypertext transfer protocol (‘HTTP’), interne protocol (‘IP’),real-time transfer protocol (‘RTP’), transmission control protocol(‘TCP’), user datagram protocol (‘UDP’), wireless application protocol(‘WAP’), or other protocol.

The cloud services provider 302 depicted in FIG. 3A may be embodied, forexample, as a system and computing environment that provides a vastarray of services to users of the cloud services provider 302 throughthe sharing of computing resources via the data communications link 304.The cloud services provider 302 may provide on-demand access to a sharedpool of configurable computing resources such as computer networks,servers, storage, applications and services, and so on.

In the example depicted in FIG. 3A, the cloud services provider 302 maybe configured to provide a variety of services to the storage system 306and users of the storage system 306 through the implementation ofvarious service models. For example, the cloud services provider 302 maybe configured to provide services through the implementation of aninfrastructure as a service (‘IaaS’) service model, through theimplementation of a platform as a service (‘PaaS’) service model,through the implementation of a software as a service (‘SaaS’) servicemodel, through the implementation of an authentication as a service(‘AaaS’) service model, through the implementation of a storage as aservice model where the cloud services provider 302 offers access to itsstorage infrastructure for use by the storage system 306 and users ofthe storage system 306, and so on.

In the example depicted in FIG. 3A, the cloud services provider 302 maybe embodied, for example, as a private cloud, as a public cloud, or as acombination of a private cloud and public cloud. In an embodiment inwhich the cloud services provider 302 is embodied as a private cloud,the cloud services provider 302 may be dedicated to providing servicesto a single organization rather than providing services to multipleorganizations. In an embodiment where the cloud services provider 302 isembodied as a public cloud, the cloud services provider 302 may provideservices to multiple organizations. In still alternative embodiments,the cloud services provider 302 may be embodied as a mix of a privateand public cloud services with a hybrid cloud deployment.

Although not explicitly depicted in FIG. 3A, readers will appreciatethat a vast amount of additional hardware components and additionalsoftware components may be necessary to facilitate the delivery of cloudservices to the storage system 306 and users of the storage system 306.For example, the storage system 306 may be coupled to (or even include)a cloud storage gateway. Such a cloud storage gateway may be embodied,for example, as hardware-based or software-based appliance that islocated on premise with the storage system 306. Such a cloud storagegateway may operate as a bridge between local applications that areexecuting on the storage system 306 and remote, cloud-based storage thatis utilized by the storage system 306. Through the use of a cloudstorage gateway, organizations may move primary iSCSI or NAS to thecloud services provider 302, thereby enabling the organization to savespace on their on-premises storage systems. Such a cloud storage gatewaymay be configured to emulate a disk array, a block-based device, a fileserver, or other storage system that can translate the SCSI commands,file server commands, or other appropriate command into REST-spaceprotocols that facilitate communications with the cloud servicesprovider 302.

In order to enable the storage system 306 and users of the storagesystem 306 to make use of the services provided by the cloud servicesprovider 302, a cloud migration process may take place during whichdata, applications, or other elements from an organization's localsystems (or even from another cloud environment) are moved to the cloudservices provider 302. In order to successfully migrate data,applications, or other elements to the cloud services provider's 302environment, middleware such as a cloud migration tool may be utilizedto bridge gaps between the cloud services provider's 302 environment andan organization's environment. In order to further enable the storagesystem 306 and users of the storage system 306 to make use of theservices provided by the cloud services provider 302, a cloudorchestrator may also be used to arrange and coordinate automated tasksin pursuit of creating a consolidated process or workflow. Such a cloudorchestrator may perform tasks such as configuring various components,whether those components are cloud components or on-premises components,as well as managing the interconnections between such components.

In the example depicted in FIG. 3A, and as described briefly above, thecloud services provider 302 may be configured to provide services to thestorage system 306 and users of the storage system 306 through the usageof a SaaS service model. For example, the cloud services provider 302may be configured to provide access to data analytics applications tothe storage system 306 and users of the storage system 306. Such dataanalytics applications may be configured, for example, to receive vastamounts of telemetry data phoned home by the storage system 306. Suchtelemetry data may describe various operating characteristics of thestorage system 306 and may be analyzed for a vast array of purposesincluding, for example, to determine the health of the storage system306, to identify workloads that are executing on the storage system 306,to predict when the storage system 306 will run out of variousresources, to recommend configuration changes, hardware or softwareupgrades, workflow migrations, or other actions that may improve theoperation of the storage system 306.

The cloud services provider 302 may also be configured to provide accessto virtualized computing environments to the storage system 306 andusers of the storage system 306. Examples of such virtualizedenvironments can include virtual machines that are created to emulate anactual computer, virtualized desktop environments that separate alogical desktop from a physical machine, virtualized file systems thatallow uniform access to different types of concrete file systems, andmany others.

Although the example depicted in FIG. 3A illustrates the storage system306 being coupled for data communications with the cloud servicesprovider 302, in other embodiments the storage system 306 may be part ofa hybrid cloud deployment in which private cloud elements (e.g., privatecloud services, on-premises infrastructure, and so on) and public cloudelements (e.g., public cloud services, infrastructure, and so on thatmay be provided by one or more cloud services providers) are combined toform a single solution, with orchestration among the various platforms.Such a hybrid cloud deployment may leverage hybrid cloud managementsoftware such as, for example, Azure™ Arc from Microsoft™, thatcentralize the management of the hybrid cloud deployment to anyinfrastructure and enable the deployment of services anywhere. In suchan example, the hybrid cloud management software may be configured tocreate, update, and delete resources (both physical and virtual) thatform the hybrid cloud deployment, to allocate compute and storage tospecific workloads, to monitor workloads and resources for performance,policy compliance, updates and patches, security status, or to perform avariety of other tasks.

Readers will appreciate that by pairing the storage systems describedherein with one or more cloud services providers, various offerings maybe enabled. For example, disaster recovery as a service (‘DRaaS’) may beprovided where cloud resources are utilized to protect applications anddata from disruption caused by disaster, including in embodiments wherethe storage systems may serve as the primary data store. In suchembodiments, a total system backup may be taken that allows for businesscontinuity in the event of system failure. In such embodiments, clouddata backup techniques (by themselves or as part of a larger DRaaSsolution) may also be integrated into an overall solution that includesthe storage systems and cloud services providers described herein.

The storage systems described herein, as well as the cloud servicesproviders, may be utilized to provide a wide array of security features.For example, the storage systems may encrypt data at rest (and data maybe sent to and from the storage systems encrypted) and may make use ofKey Management-as-a-Service (‘KMaaS’) to manage encryption keys, keysfor locking and unlocking storage devices, and so on. Likewise, clouddata security gateways or similar mechanisms may be utilized to ensurethat data stored within the storage systems does not improperly end upbeing stored in the cloud as part of a cloud data backup operation.Furthermore, microsegmentation or identity-based-segmentation may beutilized in a data center that includes the storage systems or withinthe cloud services provider, to create secure zones in data centers andcloud deployments that enables the isolation of workloads from oneanother.

For further explanation, FIG. 3B sets forth a diagram of a storagesystem 306 in accordance with some embodiments of the presentdisclosure. Although depicted in less detail, the storage system 306depicted in FIG. 3B may be similar to the storage systems describedabove with reference to FIGS. 1A-1D and FIGS. 2A-2G as the storagesystem may include many of the components described above.

The storage system 306 depicted in FIG. 3B may include a vast amount ofstorage resources 308, which may be embodied in many forms. For example,the storage resources 308 can include nano-RAM or another form ofnonvolatile random access memory that utilizes carbon nanotubesdeposited on a substrate, 3D crosspoint non-volatile memory, flashmemory including single-level cell (‘SLC’) NAND flash, multi-level cell(‘MLC’) NAND flash, triple-level cell (‘TLC’) NAND flash, quad-levelcell (‘QLC’) NAND flash, or others. Likewise, the storage resources 308may include non-volatile magnetoresistive random-access memory (‘MRAM’),including spin transfer torque (STY) MRAM. The example storage resources308 may alternatively include non-volatile phase-change memory (‘PCM’),quantum memory that allows for the storage and retrieval of photonicquantum information, resistive random-access memory (‘ReRAM’), storageclass memory (‘SCM’), or other form of storage resources, including anycombination of resources described herein. Readers will appreciate thatother forms of computer memories and storage devices may be utilized bythe storage systems described above, including DRAM, SRAM, EEPROM,universal memory, and many others. The storage resources 308 depicted inFIG. 3A may be embodied in a variety of form factors, including but notlimited to, dual in-line memory modules (‘DIMMs’), non-volatile dualin-line memory modules (‘NVDIMMs’), M.2, U.2, and others.

The storage resources 308 depicted in FIG. 3B may include various formsof SCM. SCM may effectively treat fast, non-volatile memory (e.g., NANDflash) as an extension of DRAM such that an entire dataset may betreated as an in-memory dataset that resides entirely in DRAM. SCM mayinclude non-volatile media such as, for example, NAND flash. Such NANDflash may be accessed utilizing NVMe that can use the PCIe bus as itstransport, providing for relatively low access latencies compared toolder protocols. In fact, the network protocols used for SSDs inall-flash arrays can include NVMe using Ethernet (ROCE, NVME TCP), FibreChannel (NVMe FC), InfiniBand (iWARP), and others that make it possibleto treat fast, non-volatile memory as an extension of DRAM. In view ofthe fact that DRAM is often byte-addressable and fast, non-volatilememory such as NAND flash is block-addressable, a controllersoftware/hardware stack may be needed to convert the block data to thebytes that are stored in the media. Examples of media and software thatmay be used as SCM can include, for example, 3D XPoint, Intel MemoryDrive Technology, Samsung's Z-SSD, and others.

The storage resources 308 depicted in FIG. 3B may also include racetrackmemory (also referred to as domain-wall memory). Such racetrack memorymay be embodied as a form of non-volatile, solid-state memory thatrelies on the intrinsic strength and orientation of the magnetic fieldcreated by an electron as it spins in addition to its electronic charge,in solid-state devices. Through the use of spin-coherent electriccurrent to move magnetic domains along a nanoscopic permalloy wire, thedomains may pass by magnetic read/write heads positioned near the wireas current is passed through the wire, which alter the domains to recordpatterns of bits. In order to create a racetrack memory device, manysuch wires and read/write elements may be packaged together.

The example storage system 306 depicted in FIG. 3B may implement avariety of storage architectures. For example, storage systems inaccordance with some embodiments of the present disclosure may utilizeblock storage where data is stored in blocks, and each block essentiallyacts as an individual hard drive. Storage systems in accordance withsome embodiments of the present disclosure may utilize object storage,where data is managed as objects. Each object may include the dataitself, a variable amount of metadata, and a globally unique identifier,where object storage can be implemented at multiple levels (e.g., devicelevel, system level, interface level). Storage systems in accordancewith some embodiments of the present disclosure utilize file storage inwhich data is stored in a hierarchical structure. Such data may be savedin files and folders, and presented to both the system storing it andthe system retrieving it in the same format.

The example storage system 306 depicted in FIG. 3B may be embodied as astorage system in which additional storage resources can be addedthrough the use of a scale-up model, additional storage resources can beadded through the use of a scale-out model, or through some combinationthereof. In a scale-up model, additional storage may be added by addingadditional storage devices. In a scale-out model, however, additionalstorage nodes may be added to a cluster of storage nodes, where suchstorage nodes can include additional processing resources, additionalnetworking resources, and so on.

The example storage system 306 depicted in FIG. 3B may leverage thestorage resources described above in a variety of different ways. Forexample, some portion of the storage resources may be utilized to serveas a write cache, storage resources within the storage system may beutilized as a read cache, or tiering may be achieved within the storagesystems by placing data within the storage system in accordance with oneor more tiering policies.

The storage system 306 depicted in FIG. 3B also includes communicationsresources 310 that may be useful in facilitating data communicationsbetween components within the storage system 306, as well as datacommunications between the storage system 306 and computing devices thatare outside of the storage system 306, including embodiments where thoseresources are separated by a relatively vast expanse. The communicationsresources 310 may be configured to utilize a variety of differentprotocols and data communication fabrics to facilitate datacommunications between components within the storage systems as well ascomputing devices that are outside of the storage system. For example,the communications resources 310 can include fibre channel (‘FC’)technologies such as FC fabrics and FC protocols that can transport SCSIcommands over FC network, FC over ethernet (‘FCoE’) technologies throughwhich FC frames are encapsulated and transmitted over Ethernet networks,InfiniBand (‘IB’) technologies in which a switched fabric topology isutilized to facilitate transmissions between channel adapters, NVMExpress (‘NVMe’) technologies and NVMe over fabrics (‘NVMeoF’)technologies through which non-volatile storage media attached via a PCIexpress (‘PCIe’) bus may be accessed, and others. In fact, the storagesystems described above may, directly or indirectly, make use ofneutrino communication technologies and devices through whichinformation (including binary information) is transmitted using a beamof neutrinos.

The communications resources 310 can also include mechanisms foraccessing storage resources 308 within the storage system 306 utilizingserial attached SCSI (‘SAS’), serial ATA (‘SATA’) bus interfaces forconnecting storage resources 308 within the storage system 306 to hostbus adapters within the storage system 306, interne small computersystems interface (‘iSCSI’) technologies to provide block-level accessto storage resources 308 within the storage system 306, and othercommunications resources that that may be useful in facilitating datacommunications between components within the storage system 306, as wellas data communications between the storage system 306 and computingdevices that are outside of the storage system 306.

The storage system 306 depicted in FIG. 3B also includes processingresources 312 that may be useful in useful in executing computer programinstructions and performing other computational tasks within the storagesystem 306. The processing resources 312 may include one or more ASICsthat are customized for some particular purpose as well as one or moreCPUs. The processing resources 312 may also include one or more DSPs,one or more FPGAs, one or more systems on a chip (‘SoCs’), or other formof processing resources 312. The storage system 306 may utilize thestorage resources 312 to perform a variety of tasks including, but notlimited to, supporting the execution of software resources 314 that willbe described in greater detail below.

The storage system 306 depicted in FIG. 3B also includes softwareresources 314 that, when executed by processing resources 312 within thestorage system 306, may perform a vast array of tasks. The softwareresources 314 may include, for example, one or more modules of computerprogram instructions that when executed by processing resources 312within the storage system 306 are useful in carrying out various dataprotection techniques. Such data protection techniques may be carriedout, for example, by system software executing on computer hardwarewithin the storage system, by a cloud services provider, or in otherways. Such data protection techniques can include data archiving, databackup, data replication, data snapshotting, data and database cloning,and other data protection techniques.

The software resources 314 may also include software that is useful inimplementing software-defined storage (‘SDS’). In such an example, thesoftware resources 314 may include one or more modules of computerprogram instructions that, when executed, are useful in policy-basedprovisioning and management of data storage that is independent of theunderlying hardware. Such software resources 314 may be useful inimplementing storage virtualization to separate the storage hardwarefrom the software that manages the storage hardware.

The software resources 314 may also include software that is useful infacilitating and optimizing I/O operations that are directed to thestorage system 306. For example, the software resources 314 may includesoftware modules that perform various data reduction techniques such as,for example, data compression, data deduplication, and others. Thesoftware resources 314 may include software modules that intelligentlygroup together I/O operations to facilitate better usage of theunderlying storage resource 308, software modules that perform datamigration operations to migrate from within a storage system, as well assoftware modules that perform other functions. Such software resources314 may be embodied as one or more software containers or in many otherways.

For further explanation, FIG. 3C sets forth an example of a cloud-basedstorage system 318 in accordance with some embodiments of the presentdisclosure. In the example depicted in FIG. 3C, the cloud-based storagesystem 318 is created entirely in a cloud computing environment 316 suchas, for example, Amazon Web Services (‘AWS’)™, Microsoft Azure™, GoogleCloud Platform™, IBM Cloud™, Oracle Cloud™, and others. The cloud-basedstorage system 318 may be used to provide services similar to theservices that may be provided by the storage systems described above.

The cloud-based storage system 318 depicted in FIG. 3C includes twocloud computing instances 320, 322 that each are used to support theexecution of a storage controller application 324, 326. The cloudcomputing instances 320, 322 may be embodied, for example, as instancesof cloud computing resources (e.g., virtual machines) that may beprovided by the cloud computing environment 316 to support the executionof software applications such as the storage controller application 324,326. For example, each of the cloud computing instances 320, 322 mayexecute on an Azure VM, where each Azure VM may include high speedtemporary storage that may be leveraged as a cache (e.g., as a readcache). In one embodiment, the cloud computing instances 320, 322 may beembodied as Amazon Elastic Compute Cloud (‘EC2’) instances. In such anexample, an Amazon Machine Image (‘AMI’) that includes the storagecontroller application 324, 326 may be booted to create and configure avirtual machine that may execute the storage controller application 324,326.

In the example method depicted in FIG. 3C, the storage controllerapplication 324, 326 may be embodied as a module of computer programinstructions that, when executed, carries out various storage tasks. Forexample, the storage controller application 324, 326 may be embodied asa module of computer program instructions that, when executed, carriesout the same tasks as the controllers 110A, 110B in FIG. 1A describedabove such as writing data to the cloud-based storage system 318,erasing data from the cloud-based storage system 318, retrieving datafrom the cloud-based storage system 318, monitoring and reporting ofstorage device utilization and performance, performing redundancyoperations, such as RAID or RAID-like data redundancy operations,compressing data, encrypting data, deduplicating data, and so forth.Readers will appreciate that because there are two cloud computinginstances 320, 322 that each include the storage controller application324, 326, in some embodiments one cloud computing instance 320 mayoperate as the primary controller as described above while the othercloud computing instance 322 may operate as the secondary controller asdescribed above. Readers will appreciate that the storage controllerapplication 324, 326 depicted in FIG. 3C may include identical sourcecode that is executed within different cloud computing instances 320,322 such as distinct EC2 instances.

Readers will appreciate that other embodiments that do not include aprimary and secondary controller are within the scope of the presentdisclosure. For example, each cloud computing instance 320, 322 mayoperate as a primary controller for some portion of the address spacesupported by the cloud-based storage system 318, each cloud computinginstance 320, 322 may operate as a primary controller where theservicing of I/O operations directed to the cloud-based storage system318 are divided in some other way, and so on. In fact, in otherembodiments where costs savings may be prioritized over performancedemands, only a single cloud computing instance may exist that containsthe storage controller application.

The cloud-based storage system 318 depicted in FIG. 3C includes cloudcomputing instances 340 a, 340 b, 340 n with local storage 330, 334,338. The cloud computing instances 340 a, 340 b, 340 n may be embodied,for example, as instances of cloud computing resources that may beprovided by the cloud computing environment 316 to support the executionof software applications. The cloud computing instances 340 a, 340 b,340 n of FIG. 3C may differ from the cloud computing instances 320, 322described above as the cloud computing instances 340 a, 340 b, 340 n ofFIG. 3C have local storage 330, 334, 338 resources whereas the cloudcomputing instances 320, 322 that support the execution of the storagecontroller application 324, 326 need not have local storage resources.The cloud computing instances 340 a, 340 b, 340 n with local storage330, 334, 338 may be embodied, for example, as EC2 M5 instances thatinclude one or more SSDs, as EC2 R5 instances that include one or moreSSDs, as EC2 I3 instances that include one or more SSDs, and so on. Insome embodiments, the local storage 330, 334, 338 must be embodied assolid-state storage (e.g., SSDs) rather than storage that makes use ofhard disk drives.

In the example depicted in FIG. 3C, each of the cloud computinginstances 340 a, 340 b, 340 n with local storage 330, 334, 338 caninclude a software daemon 328, 332, 336 that, when executed by a cloudcomputing instance 340 a, 340 b, 340 n can present itself to the storagecontroller applications 324, 326 as if the cloud computing instance 340a, 340 b, 340 n were a physical storage device (e.g., one or more SSDs).In such an example, the software daemon 328, 332, 336 may includecomputer program instructions similar to those that would normally becontained on a storage device such that the storage controllerapplications 324, 326 can send and receive the same commands that astorage controller would send to storage devices. In such a way, thestorage controller applications 324, 326 may include code that isidentical to (or substantially identical to) the code that would beexecuted by the controllers in the storage systems described above. Inthese and similar embodiments, communications between the storagecontroller applications 324, 326 and the cloud computing instances 340a, 340 b, 340 n with local storage 330, 334, 338 may utilize iSCSI, NVMeover TCP, messaging, a custom protocol, or in some other mechanism.

In the example depicted in FIG. 3C, each of the cloud computinginstances 340 a, 340 b, 340 n with local storage 330, 334, 338 may alsobe coupled to block storage 342, 344, 346 that is offered by the cloudcomputing environment 316 such as, for example, as Amazon Elastic BlockStore (‘EBS’) volumes. In such an example, the block storage 342, 344,346 that is offered by the cloud computing environment 316 may beutilized in a manner that is similar to how the NVRAM devices describedabove are utilized, as the software daemon 328, 332, 336 (or some othermodule) that is executing within a particular cloud comping instance 340a, 340 b, 340 n may, upon receiving a request to write data, initiate awrite of the data to its attached EBS volume as well as a write of thedata to its local storage 330, 334, 338 resources. In some alternativeembodiments, data may only be written to the local storage 330, 334, 338resources within a particular cloud comping instance 340 a, 340 b, 340n. In an alternative embodiment, rather than using the block storage342, 344, 346 that is offered by the cloud computing environment 316 asNVRAM, actual RAM on each of the cloud computing instances 340 a, 340 b,340 n with local storage 330, 334, 338 may be used as NVRAM, therebydecreasing network utilization costs that would be associated with usingan EBS volume as the NVRAM. In yet another embodiment, high performanceblock storage resources such as one or more Azure Ultra Disks may beutilized as the NVRAM.

When a request to write data is received by a particular cloud computinginstance 340 a, 340 b, 340 n with local storage 330, 334, 338, thesoftware daemon 328, 332, 336 may be configured to not only write thedata to its own local storage 330, 334, 338 resources and anyappropriate block storage 342, 344, 346 resources, but the softwaredaemon 328, 332, 336 may also be configured to write the data tocloud-based object storage 348 that is attached to the particular cloudcomputing instance 340 a, 340 b, 340 n. The cloud-based object storage348 that is attached to the particular cloud computing instance 340 a,340 b, 340 n may be embodied, for example, as Amazon Simple StorageService (‘S3’). In other embodiments, the cloud computing instances 320,322 that each include the storage controller application 324, 326 mayinitiate the storage of the data in the local storage 330, 334, 338 ofthe cloud computing instances 340 a, 340 b, 340 n and the cloud-basedobject storage 348. In other embodiments, rather than using both thecloud computing instances 340 a, 340 b, 340 n with local storage 330,334, 338 (also referred to herein as ‘virtual drives’) and thecloud-based object storage 348 to store data, a persistent storage layermay be implemented in other ways. For example, one or more Azure Ultradisks may be used to persistently store data (e.g., after the data hasbeen written to the NVRAM layer). In an embodiment where one or moreAzure Ultra disks may be used to persistently store data, the usage of acloud-based object storage 348 may be eliminated such that data is onlystored persistently in the Azure Ultra disks without also writing thedata to an object storage layer.

While the local storage 330, 334, 338 resources and the block storage342, 344, 346 resources that are utilized by the cloud computinginstances 340 a, 340 b, 340 n may support block-level access, thecloud-based object storage 348 that is attached to the particular cloudcomputing instance 340 a, 340 b, 340 n supports only object-basedaccess. The software daemon 328, 332, 336 may therefore be configured totake blocks of data, package those blocks into objects, and write theobjects to the cloud-based object storage 348 that is attached to theparticular cloud computing instance 340 a, 340 b, 340 n.

In some embodiments, all data that is stored by the cloud-based storagesystem 318 may be stored in both: 1) the cloud-based object storage 348,and 2) at least one of the local storage 330, 334, 338 resources orblock storage 342, 344, 346 resources that are utilized by the cloudcomputing instances 340 a, 340 b, 340 n. In such embodiments, the localstorage 330, 334, 338 resources and block storage 342, 344, 346resources that are utilized by the cloud computing instances 340 a, 340b, 340 n may effectively operate as cache that generally includes alldata that is also stored in S3, such that all reads of data may beserviced by the cloud computing instances 340 a, 340 b, 340 n withoutrequiring the cloud computing instances 340 a, 340 b, 340 n to accessthe cloud-based object storage 348. Readers will appreciate that inother embodiments, however, all data that is stored by the cloud-basedstorage system 318 may be stored in the cloud-based object storage 348,but less than all data that is stored by the cloud-based storage system318 may be stored in at least one of the local storage 330, 334, 338resources or block storage 342, 344, 346 resources that are utilized bythe cloud computing instances 340 a, 340 b, 340 n. In such an example,various policies may be utilized to determine which subset of the datathat is stored by the cloud-based storage system 318 should reside inboth: 1) the cloud-based object storage 348, and 2) at least one of thelocal storage 330, 334, 338 resources or block storage 342, 344, 346resources that are utilized by the cloud computing instances 340 a, 340b, 340 n.

One or more modules of computer program instructions that are executingwithin the cloud-based storage system 318 (e.g., a monitoring modulethat is executing on its own EC2 instance) may be designed to handle thefailure of one or more of the cloud computing instances 340 a, 340 b,340 n with local storage 330, 334, 338. In such an example, themonitoring module may handle the failure of one or more of the cloudcomputing instances 340 a, 340 b, 340 n with local storage 330, 334, 338by creating one or more new cloud computing instances with localstorage, retrieving data that was stored on the failed cloud computinginstances 340 a, 340 b, 340 n from the cloud-based object storage 348,and storing the data retrieved from the cloud-based object storage 348in local storage on the newly created cloud computing instances. Readerswill appreciate that many variants of this process may be implemented.

Readers will appreciate that various performance aspects of thecloud-based storage system 318 may be monitored (e.g., by a monitoringmodule that is executing in an EC2 instance) such that the cloud-basedstorage system 318 can be scaled-up or scaled-out as needed. Forexample, if the cloud computing instances 320, 322 that are used tosupport the execution of a storage controller application 324, 326 areundersized and not sufficiently servicing the I/O requests that areissued by users of the cloud-based storage system 318, a monitoringmodule may create a new, more powerful cloud computing instance (e.g., acloud computing instance of a type that includes more processing power,more memory, etc. . . . ) that includes the storage controllerapplication such that the new, more powerful cloud computing instancecan begin operating as the primary controller. Likewise, if themonitoring module determines that the cloud computing instances 320, 322that are used to support the execution of a storage controllerapplication 324, 326 are oversized and that cost savings could be gainedby switching to a smaller, less powerful cloud computing instance, themonitoring module may create a new, less powerful (and less expensive)cloud computing instance that includes the storage controllerapplication such that the new, less powerful cloud computing instancecan begin operating as the primary controller.

The storage systems described above may carry out intelligent databackup techniques through which data stored in the storage system may becopied and stored in a distinct location to avoid data loss in the eventof equipment failure or some other form of catastrophe. For example, thestorage systems described above may be configured to examine each backupto avoid restoring the storage system to an undesirable state. Consideran example in which malware infects the storage system. In such anexample, the storage system may include software resources 314 that canscan each backup to identify backups that were captured before themalware infected the storage system and those backups that were capturedafter the malware infected the storage system. In such an example, thestorage system may restore itself from a backup that does not includethe malware—or at least not restore the portions of a backup thatcontained the malware. In such an example, the storage system mayinclude software resources 314 that can scan each backup to identify thepresences of malware (or a virus, or some other undesirable), forexample, by identifying write operations that were serviced by thestorage system and originated from a network subnet that is suspected tohave delivered the malware, by identifying write operations that wereserviced by the storage system and originated from a user that issuspected to have delivered the malware, by identifying write operationsthat were serviced by the storage system and examining the content ofthe write operation against fingerprints of the malware, and in manyother ways.

Readers will further appreciate that the backups (often in the form ofone or more snapshots) may also be utilized to perform rapid recovery ofthe storage system. Consider an example in which the storage system isinfected with ransomware that locks users out of the storage system. Insuch an example, software resources 314 within the storage system may beconfigured to detect the presence of ransomware and may be furtherconfigured to restore the storage system to a point-in-time, using theretained backups, prior to the point-in-time at which the ransomwareinfected the storage system. In such an example, the presence ofransomware may be explicitly detected through the use of software toolsutilized by the system, through the use of a key (e.g., a USB drive)that is inserted into the storage system, or in a similar way. Likewise,the presence of ransomware may be inferred in response to systemactivity meeting a predetermined fingerprint such as, for example, noreads or writes coming into the system for a predetermined period oftime.

Readers will appreciate that the various components described above maybe grouped into one or more optimized computing packages as convergedinfrastructures. Such converged infrastructures may include pools ofcomputers, storage and networking resources that can be shared bymultiple applications and managed in a collective manner usingpolicy-driven processes. Such converged infrastructures may beimplemented with a converged infrastructure reference architecture, withstandalone appliances, with a software driven hyper-converged approach(e.g., hyper-converged infrastructures), or in other ways.

Readers will appreciate that the storage systems described in thisdisclosure may be useful for supporting various types of softwareapplications. In fact, the storage systems may be ‘application aware’ inthe sense that the storage systems may obtain, maintain, or otherwisehave access to information describing connected applications (e.g.,applications that utilize the storage systems) to optimize the operationof the storage system based on intelligence about the applications andtheir utilization patterns. For example, the storage system may optimizedata layouts, optimize caching behaviors, optimize ‘QoS’ levels, orperform some other optimization that is designed to improve the storageperformance that is experienced by the application.

As an example of one type of application that may be supported by thestorage systems describe herein, the storage system 306 may be useful insupporting artificial intelligence (‘AI’) applications, databaseapplications, XOps projects (e.g., DevOps projects, DataOps projects,MLOps projects, ModelOps projects, PlatformOps projects), electronicdesign automation tools, event-driven software applications, highperformance computing applications, simulation applications, high-speeddata capture and analysis applications, machine learning applications,media production applications, media serving applications, picturearchiving and communication systems (‘PACS’) applications, softwaredevelopment applications, virtual reality applications, augmentedreality applications, and many other types of applications by providingstorage resources to such applications.

In view of the fact that the storage systems include compute resources,storage resources, and a wide variety of other resources, the storagesystems may be well suited to support applications that are resourceintensive such as, for example, AI applications. AI applications may bedeployed in a variety of fields, including: predictive maintenance inmanufacturing and related fields, healthcare applications such aspatient data & risk analytics, retail and marketing deployments (e.g.,search advertising, social media advertising), supply chains solutions,fintech solutions such as business analytics & reporting tools,operational deployments such as real-time analytics tools, applicationperformance management tools, IT infrastructure management tools, andmany others.

Such AI applications may enable devices to perceive their environmentand take actions that maximize their chance of success at some goal.Examples of such AI applications can include IBM Watson™, MicrosoftOxford™, Google DeepMind™, Baidu Minwa™, and others.

The storage systems described above may also be well suited to supportother types of applications that are resource intensive such as, forexample, machine learning applications. Machine learning applicationsmay perform various types of data analysis to automate analytical modelbuilding. Using algorithms that iteratively learn from data, machinelearning applications can enable computers to learn without beingexplicitly programmed. One particular area of machine learning isreferred to as reinforcement learning, which involves taking suitableactions to maximize reward in a particular situation.

In addition to the resources already described, the storage systemsdescribed above may also include graphics processing units (‘GPUs’),occasionally referred to as visual processing unit (‘VPUs’). Such GPUsmay be embodied as specialized electronic circuits that rapidlymanipulate and alter memory to accelerate the creation of images in aframe buffer intended for output to a display device. Such GPUs may beincluded within any of the computing devices that are part of thestorage systems described above, including as one of many individuallyscalable components of a storage system, where other examples ofindividually scalable components of such storage system can includestorage components, memory components, compute components (e.g., CPUs,FPGAs, ASICs), networking components, software components, and others.In addition to GPUs, the storage systems described above may alsoinclude neural network processors (‘NNPs’) for use in various aspects ofneural network processing. Such NNPs may be used in place of (or inaddition to) GPUs and may also be independently scalable.

As described above, the storage systems described herein may beconfigured to support artificial intelligence applications, machinelearning applications, big data analytics applications, and many othertypes of applications. The rapid growth in these sort of applications isbeing driven by three technologies: deep learning (DL), GPU processors,and Big Data. Deep learning is a computing model that makes use ofmassively parallel neural networks inspired by the human brain. Insteadof experts handcrafting software, a deep learning model writes its ownsoftware by learning from lots of examples. Such GPUs may includethousands of cores that are well-suited to run algorithms that looselyrepresent the parallel nature of the human brain.

Advances in deep neural networks, including the development ofmulti-layer neural networks, have ignited a new wave of algorithms andtools for data scientists to tap into their data with artificialintelligence (AI). With improved algorithms, larger data sets, andvarious frameworks (including open-source software libraries for machinelearning across a range of tasks), data scientists are tackling new usecases like autonomous driving vehicles, natural language processing andunderstanding, computer vision, machine reasoning, strong AI, and manyothers. Applications of AI techniques have materialized in a wide arrayof products include, for example, Amazon Echo's speech recognitiontechnology that allows users to talk to their machines, GoogleTranslate™ which allows for machine-based language translation,Spotify's Discover Weekly that provides recommendations on new songs andartists that a user may like based on the user's usage and trafficanalysis, Quill's text generation offering that takes structured dataand turns it into narrative stories, Chatbots that provide real-time,contextually specific answers to questions in a dialog format, and manyothers.

Data is the heart of modern AI and deep learning algorithms. Beforetraining can begin, one problem that must be addressed revolves aroundcollecting the labeled data that is crucial for training an accurate AImodel. A full scale AI deployment may be required to continuouslycollect, clean, transform, label, and store large amounts of data.Adding additional high quality data points directly translates to moreaccurate models and better insights. Data samples may undergo a seriesof processing steps including, but not limited to: 1) ingesting the datafrom an external source into the training system and storing the data inraw form, 2) cleaning and transforming the data in a format convenientfor training, including linking data samples to the appropriate label,3) exploring parameters and models, quickly testing with a smallerdataset, and iterating to converge on the most promising models to pushinto the production cluster, 4) executing training phases to selectrandom batches of input data, including both new and older samples, andfeeding those into production GPU servers for computation to updatemodel parameters, and 5) evaluating including using a holdback portionof the data not used in training in order to evaluate model accuracy onthe holdout data. This lifecycle may apply for any type of parallelizedmachine learning, not just neural networks or deep learning. Forexample, standard machine learning frameworks may rely on CPUs insteadof GPUs but the data ingest and training workflows may be the same.Readers will appreciate that a single shared storage data hub creates acoordination point throughout the lifecycle without the need for extradata copies among the ingest, preprocessing, and training stages. Rarelyis the ingested data used for only one purpose, and shared storage givesthe flexibility to train multiple different models or apply traditionalanalytics to the data.

Readers will appreciate that each stage in the AI data pipeline may havevarying requirements from the data hub (e.g., the storage system orcollection of storage systems). Scale-out storage systems must deliveruncompromising performance for all manner of access types andpatterns—from small, metadata-heavy to large files, from random tosequential access patterns, and from low to high concurrency. Thestorage systems described above may serve as an ideal AI data hub as thesystems may service unstructured workloads. In the first stage, data isideally ingested and stored on to the same data hub that followingstages will use, in order to avoid excess data copying. The next twosteps can be done on a standard compute server that optionally includesa GPU, and then in the fourth and last stage, full training productionjobs are run on powerful GPU-accelerated servers. Often, there is aproduction pipeline alongside an experimental pipeline operating on thesame dataset. Further, the GPU-accelerated servers can be usedindependently for different models or joined together to train on onelarger model, even spanning multiple systems for distributed training.If the shared storage tier is slow, then data must be copied to localstorage for each phase, resulting in wasted time staging data ontodifferent servers. The ideal data hub for the AI training pipelinedelivers performance similar to data stored locally on the server nodewhile also having the simplicity and performance to enable all pipelinestages to operate concurrently.

In order for the storage systems described above to serve as a data hubor as part of an AI deployment, in some embodiments the storage systemsmay be configured to provide DMA between storage devices that areincluded in the storage systems and one or more GPUs that are used in anAI or big data analytics pipeline. The one or more GPUs may be coupledto the storage system, for example, via NVMe-over-Fabrics (‘NVMe-oF’)such that bottlenecks such as the host CPU can be bypassed and thestorage system (or one of the components contained therein) can directlyaccess GPU memory. In such an example, the storage systems may leverageAPI hooks to the GPUs to transfer data directly to the GPUs. Forexample, the GPUs may be embodied as Nvidia™ GPUs and the storagesystems may support GPUDirect Storage (‘GDS’) software, or have similarproprietary software, that enables the storage system to transfer datato the GPUs via RDMA or similar mechanism.

Although the preceding paragraphs discuss deep learning applications,readers will appreciate that the storage systems described herein mayalso be part of a distributed deep learning (‘DDL’) platform to supportthe execution of DDL algorithms. The storage systems described above mayalso be paired with other technologies such as TensorFlow, anopen-source software library for dataflow programming across a range oftasks that may be used for machine learning applications such as neuralnetworks, to facilitate the development of such machine learning models,applications, and so on.

The storage systems described above may also be used in a neuromorphiccomputing environment. Neuromorphic computing is a form of computingthat mimics brain cells. To support neuromorphic computing, anarchitecture of interconnected “neurons” replace traditional computingmodels with low-powered signals that go directly between neurons formore efficient computation. Neuromorphic computing may make use ofvery-large-scale integration (VLSI) systems containing electronic analogcircuits to mimic neuro-biological architectures present in the nervoussystem, as well as analog, digital, mixed-mode analog/digital VLSI, andsoftware systems that implement models of neural systems for perception,motor control, or multisensory integration.

Readers will appreciate that the storage systems described above may beconfigured to support the storage or use of (among other types of data)blockchains and derivative items such as, for example, open sourceblockchains and related tools that are part of the IBM™ Hyperledgerproject, permissioned blockchains in which a certain number of trustedparties are allowed to access the block chain, blockchain products thatenable developers to build their own distributed ledger projects, andothers. Blockchains and the storage systems described herein may beleveraged to support on-chain storage of data as well as off-chainstorage of data.

Off-chain storage of data can be implemented in a variety of ways andcan occur when the data itself is not stored within the blockchain. Forexample, in one embodiment, a hash function may be utilized and the dataitself may be fed into the hash function to generate a hash value. Insuch an example, the hashes of large pieces of data may be embeddedwithin transactions, instead of the data itself. Readers will appreciatethat, in other embodiments, alternatives to blockchains may be used tofacilitate the decentralized storage of information. For example, onealternative to a blockchain that may be used is a blockweave. Whileconventional blockchains store every transaction to achieve validation,a blockweave permits secure decentralization without the usage of theentire chain, thereby enabling low cost on-chain storage of data. Suchblockweaves may utilize a consensus mechanism that is based on proof ofaccess (PoA) and proof of work (PoW).

The storage systems described above may, either alone or in combinationwith other computing devices, be used to support in-memory computingapplications. In-memory computing involves the storage of information inRAM that is distributed across a cluster of computers. Readers willappreciate that the storage systems described above, especially thosethat are configurable with customizable amounts of processing resources,storage resources, and memory resources (e.g., those systems in whichblades that contain configurable amounts of each type of resource), maybe configured in a way so as to provide an infrastructure that cansupport in-memory computing. Likewise, the storage systems describedabove may include component parts (e.g., NVDIMMs, 3D crosspoint storagethat provide fast random access memory that is persistent) that canactually provide for an improved in-memory computing environment ascompared to in-memory computing environments that rely on RAMdistributed across dedicated servers.

In some embodiments, the storage systems described above may beconfigured to operate as a hybrid in-memory computing environment thatincludes a universal interface to all storage media (e.g., RAM, flashstorage, 3D crosspoint storage). In such embodiments, users may have noknowledge regarding the details of where their data is stored but theycan still use the same full, unified API to address data. In suchembodiments, the storage system may (in the background) move data to thefastest layer available—including intelligently placing the data independence upon various characteristics of the data or in dependenceupon some other heuristic. In such an example, the storage systems mayeven make use of existing products such as Apache Ignite and GridGain tomove data between the various storage layers, or the storage systems maymake use of custom software to move data between the various storagelayers. The storage systems described herein may implement variousoptimizations to improve the performance of in-memory computing such as,for example, having computations occur as close to the data as possible.

Readers will further appreciate that in some embodiments, the storagesystems described above may be paired with other resources to supportthe applications described above. For example, one infrastructure couldinclude primary compute in the form of servers and workstations whichspecialize in using General-purpose computing on graphics processingunits (‘GPGPU’) to accelerate deep learning applications that areinterconnected into a computation engine to train parameters for deepneural networks. Each system may have Ethernet external connectivity,InfiniBand external connectivity, some other form of externalconnectivity, or some combination thereof. In such an example, the GPUscan be grouped for a single large training or used independently totrain multiple models. The infrastructure could also include a storagesystem such as those described above to provide, for example, ascale-out all-flash file or object store through which data can beaccessed via high-performance protocols such as NFS, S3, and so on. Theinfrastructure can also include, for example, redundant top-of-rackEthernet switches connected to storage and compute via ports in MLAGport channels for redundancy. The infrastructure could also includeadditional compute in the form of whitebox servers, optionally withGPUs, for data ingestion, pre-processing, and model debugging. Readerswill appreciate that additional infrastructures are also be possible.

Readers will appreciate that the storage systems described above, eitheralone or in coordination with other computing machinery may beconfigured to support other AI related tools. For example, the storagesystems may make use of tools like ONXX or other open neural networkexchange formats that make it easier to transfer models written indifferent AI frameworks. Likewise, the storage systems may be configuredto support tools like Amazon's Gluon that allow developers to prototype,build, and train deep learning models. In fact, the storage systemsdescribed above may be part of a larger platform, such as IBM™ CloudPrivate for Data, that includes integrated data science, dataengineering and application building services.

Readers will further appreciate that the storage systems described abovemay also be deployed as an edge solution. Such an edge solution may bein place to optimize cloud computing systems by performing dataprocessing at the edge of the network, near the source of the data. Edgecomputing can push applications, data and computing power (i.e.,services) away from centralized points to the logical extremes of anetwork. Through the use of edge solutions such as the storage systemsdescribed above, computational tasks may be performed using the computeresources provided by such storage systems, data may be storage usingthe storage resources of the storage system, and cloud-based servicesmay be accessed through the use of various resources of the storagesystem (including networking resources). By performing computationaltasks on the edge solution, storing data on the edge solution, andgenerally making use of the edge solution, the consumption of expensivecloud-based resources may be avoided and, in fact, performanceimprovements may be experienced relative to a heavier reliance oncloud-based resources.

While many tasks may benefit from the utilization of an edge solution,some particular uses may be especially suited for deployment in such anenvironment. For example, devices like drones, autonomous cars, robots,and others may require extremely rapid processing—so fast, in fact, thatsending data up to a cloud environment and back to receive dataprocessing support may simply be too slow. As an additional example,some IoT devices such as connected video cameras may not be well-suitedfor the utilization of cloud-based resources as it may be impractical(not only from a privacy perspective, security perspective, or afinancial perspective) to send the data to the cloud simply because ofthe pure volume of data that is involved. As such, many tasks thatreally on data processing, storage, or communications may be bettersuited by platforms that include edge solutions such as the storagesystems described above.

The storage systems described above may alone, or in combination withother computing resources, serves as a network edge platform thatcombines compute resources, storage resources, networking resources,cloud technologies and network virtualization technologies, and so on.As part of the network, the edge may take on characteristics similar toother network facilities, from the customer premise and backhaulaggregation facilities to Points of Presence (PoPs) and regional datacenters. Readers will appreciate that network workloads, such as VirtualNetwork Functions (VNFs) and others, will reside on the network edgeplatform. Enabled by a combination of containers and virtual machines,the network edge platform may rely on controllers and schedulers thatare no longer geographically co-located with the data processingresources. The functions, as microservices, may split into controlplanes, user and data planes, or even state machines, allowing forindependent optimization and scaling techniques to be applied. Such userand data planes may be enabled through increased accelerators, boththose residing in server platforms, such as FPGAs and Smart NICs, andthrough SDN-enabled merchant silicon and programmable ASICs.

The storage systems described above may also be optimized for use in bigdata analytics, including being leveraged as part of a composable dataanalytics pipeline where containerized analytics architectures, forexample, make analytics capabilities more composable. Big data analyticsmay be generally described as the process of examining large and varieddata sets to uncover hidden patterns, unknown correlations, markettrends, customer preferences and other useful information that can helporganizations make more-informed business decisions. As part of thatprocess, semi-structured and unstructured data such as, for example,internet clickstream data, web server logs, social media content, textfrom customer emails and survey responses, mobile-phone call-detailrecords, IoT sensor data, and other data may be converted to astructured form.

The storage systems described above may also support (includingimplementing as a system interface) applications that perform tasks inresponse to human speech. For example, the storage systems may supportthe execution intelligent personal assistant applications such as, forexample, Amazon's Alexa™, Apple Siri™, Google Voice™, Samsung Bixby™,Microsoft Cortana™, and others. While the examples described in theprevious sentence make use of voice as input, the storage systemsdescribed above may also support chatbots, talkbots, chatterbots, orartificial conversational entities or other applications that areconfigured to conduct a conversation via auditory or textual methods.Likewise, the storage system may actually execute such an application toenable a user such as a system administrator to interact with thestorage system via speech. Such applications are generally capable ofvoice interaction, music playback, making to-do lists, setting alarms,streaming podcasts, playing audiobooks, and providing weather, traffic,and other real time information, such as news, although in embodimentsin accordance with the present disclosure, such applications may beutilized as interfaces to various system management operations.

The storage systems described above may also implement AI platforms fordelivering on the vision of self-driving storage. Such AI platforms maybe configured to deliver global predictive intelligence by collectingand analyzing large amounts of storage system telemetry data points toenable effortless management, analytics and support. In fact, suchstorage systems may be capable of predicting both capacity andperformance, as well as generating intelligent advice on workloaddeployment, interaction and optimization. Such AI platforms may beconfigured to scan all incoming storage system telemetry data against alibrary of issue fingerprints to predict and resolve incidents inreal-time, before they impact customer environments, and captureshundreds of variables related to performance that are used to forecastperformance load.

The storage systems described above may support the serialized orsimultaneous execution of artificial intelligence applications, machinelearning applications, data analytics applications, datatransformations, and other tasks that collectively may form an AIladder. Such an AI ladder may effectively be formed by combining suchelements to form a complete data science pipeline, where existdependencies between elements of the AI ladder. For example, AI mayrequire that some form of machine learning has taken place, machinelearning may require that some form of analytics has taken place,analytics may require that some form of data and informationarchitecting has taken place, and so on. As such, each element may beviewed as a rung in an AI ladder that collectively can form a completeand sophisticated AI solution.

The storage systems described above may also, either alone or incombination with other computing environments, be used to deliver an AIeverywhere experience where AI permeates wide and expansive aspects ofbusiness and life. For example, AI may play an important role in thedelivery of deep learning solutions, deep reinforcement learningsolutions, artificial general intelligence solutions, autonomousvehicles, cognitive computing solutions, commercial UAVs or drones,conversational user interfaces, enterprise taxonomies, ontologymanagement solutions, machine learning solutions, smart dust, smartrobots, smart workplaces, and many others.

The storage systems described above may also, either alone or incombination with other computing environments, be used to deliver a widerange of transparently immersive experiences (including those that usedigital twins of various “things” such as people, places, processes,systems, and so on) where technology can introduce transparency betweenpeople, businesses, and things. Such transparently immersive experiencesmay be delivered as augmented reality technologies, connected homes,virtual reality technologies, brain-computer interfaces, humanaugmentation technologies, nanotube electronics, volumetric displays, 4Dprinting technologies, or others.

The storage systems described above may also, either alone or incombination with other computing environments, be used to support a widevariety of digital platforms. Such digital platforms can include, forexample, 5G wireless systems and platforms, digital twin platforms, edgecomputing platforms, IoT platforms, quantum computing platforms,serverless PaaS, software-defined security, neuromorphic computingplatforms, and so on.

The storage systems described above may also be part of a multi-cloudenvironment in which multiple cloud computing and storage services aredeployed in a single heterogeneous architecture. In order to facilitatethe operation of such a multi-cloud environment, DevOps tools may bedeployed to enable orchestration across clouds. Likewise, continuousdevelopment and continuous integration tools may be deployed tostandardize processes around continuous integration and delivery, newfeature rollout and provisioning cloud workloads. By standardizing theseprocesses, a multi-cloud strategy may be implemented that enables theutilization of the best provider for each workload.

The storage systems described above may be used as a part of a platformto enable the use of crypto-anchors that may be used to authenticate aproduct's origins and contents to ensure that it matches a blockchainrecord associated with the product. Similarly, as part of a suite oftools to secure data stored on the storage system, the storage systemsdescribed above may implement various encryption technologies andschemes, including lattice cryptography. Lattice cryptography caninvolve constructions of cryptographic primitives that involve lattices,either in the construction itself or in the security proof. Unlikepublic-key schemes such as the RSA, Diffie-Hellman or Elliptic-Curvecryptosystems, which are easily attacked by a quantum computer, somelattice-based constructions appear to be resistant to attack by bothclassical and quantum computers.

A quantum computer is a device that performs quantum computing. Quantumcomputing is computing using quantum-mechanical phenomena, such assuperposition and entanglement. Quantum computers differ fromtraditional computers that are based on transistors, as such traditionalcomputers require that data be encoded into binary digits (bits), eachof which is always in one of two definite states (0 or 1). In contrastto traditional computers, quantum computers use quantum bits, which canbe in superpositions of states. A quantum computer maintains a sequenceof qubits, where a single qubit can represent a one, a zero, or anyquantum superposition of those two qubit states. A pair of qubits can bein any quantum superposition of 4 states, and three qubits in anysuperposition of 8 states. A quantum computer with n qubits cangenerally be in an arbitrary superposition of up to 2{circumflex over( )}n different states simultaneously, whereas a traditional computercan only be in one of these states at any one time. A quantum Turingmachine is a theoretical model of such a computer.

The storage systems described above may also be paired withFPGA-accelerated servers as part of a larger AI or ML infrastructure.Such FPGA-accelerated servers may reside near (e.g., in the same datacenter) the storage systems described above or even incorporated into anappliance that includes one or more storage systems, one or moreFPGA-accelerated servers, networking infrastructure that supportscommunications between the one or more storage systems and the one ormore FPGA-accelerated servers, as well as other hardware and softwarecomponents. Alternatively, FPGA-accelerated servers may reside within acloud computing environment that may be used to perform compute-relatedtasks for AI and ML jobs. Any of the embodiments described above may beused to collectively serve as a FPGA-based AI or ML platform. Readerswill appreciate that, in some embodiments of the FPGA-based AI or MLplatform, the FPGAs that are contained within the FPGA-acceleratedservers may be reconfigured for different types of ML models (e.g.,LSTMs, CNNs, GRUs). The ability to reconfigure the FPGAs that arecontained within the FPGA-accelerated servers may enable theacceleration of a ML or AI application based on the most optimalnumerical precision and memory model being used. Readers will appreciatethat by treating the collection of FPGA-accelerated servers as a pool ofFPGAs, any CPU in the data center may utilize the pool of FPGAs as ashared hardware microservice, rather than limiting a server to dedicatedaccelerators plugged into it.

The FPGA-accelerated servers and the GPU-accelerated servers describedabove may implement a model of computing where, rather than keeping asmall amount of data in a CPU and running a long stream of instructionsover it as occurred in more traditional computing models, the machinelearning model and parameters are pinned into the high-bandwidth on-chipmemory with lots of data streaming though the high-bandwidth on-chipmemory. FPGAs may even be more efficient than GPUs for this computingmodel, as the FPGAs can be programmed with only the instructions neededto run this kind of computing model.

The storage systems described above may be configured to provideparallel storage, for example, through the use of a parallel file systemsuch as BeeGFS. Such parallel files systems may include a distributedmetadata architecture. For example, the parallel file system may includea plurality of metadata servers across which metadata is distributed, aswell as components that include services for clients and storageservers.

The systems described above can support the execution of a wide array ofsoftware applications. Such software applications can be deployed in avariety of ways, including container-based deployment models.Containerized applications may be managed using a variety of tools. Forexample, containerized applications may be managed using Docker Swarm,Kubernetes, and others. Containerized applications may be used tofacilitate a serverless, cloud native computing deployment andmanagement model for software applications. In support of a serverless,cloud native computing deployment and management model for softwareapplications, containers may be used as part of an event handlingmechanisms (e.g., AWS Lambdas) such that various events cause acontainerized application to be spun up to operate as an event handler.

The systems described above may be deployed in a variety of ways,including being deployed in ways that support fifth generation (‘5G’)networks. 5G networks may support substantially faster datacommunications than previous generations of mobile communicationsnetworks and, as a consequence may lead to the disaggregation of dataand computing resources as modern massive data centers may become lessprominent and may be replaced, for example, by more-local, micro datacenters that are close to the mobile-network towers. The systemsdescribed above may be included in such local, micro data centers andmay be part of or paired to multi-access edge computing (‘MEC’) systems.Such MEC systems may enable cloud computing capabilities and an ITservice environment at the edge of the cellular network. By runningapplications and performing related processing tasks closer to thecellular customer, network congestion may be reduced and applicationsmay perform better.

The storage systems described above may also be configured to implementNVMe Zoned Namespaces. Through the use of NVMe Zoned Namespaces, thelogical address space of a namespace is divided into zones. Each zoneprovides a logical block address range that must be written sequentiallyand explicitly reset before rewriting, thereby enabling the creation ofnamespaces that expose the natural boundaries of the device and offloadmanagement of internal mapping tables to the host. In order to implementNVMe Zoned Name Spaces (‘ZNS’), ZNS SSDs or some other form of zonedblock devices may be utilized that expose a namespace logical addressspace using zones. With the zones aligned to the internal physicalproperties of the device, several inefficiencies in the placement ofdata can be eliminated. In such embodiments, each zone may be mapped,for example, to a separate application such that functions like wearlevelling and garbage collection could be performed on a per-zone orper-application basis rather than across the entire device. In order tosupport ZNS, the storage controllers described herein may be configuredwith to interact with zoned block devices through the usage of, forexample, the Linux™ kernel zoned block device interface or other tools.

The storage systems described above may also be configured to implementzoned storage in other ways such as, for example, through the usage ofshingled magnetic recording (SMR) storage devices. In examples wherezoned storage is used, device-managed embodiments may be deployed wherethe storage devices hide this complexity by managing it in the firmware,presenting an interface like any other storage device. Alternatively,zoned storage may be implemented via a host-managed embodiment thatdepends on the operating system to know how to handle the drive, andonly write sequentially to certain regions of the drive. Zoned storagemay similarly be implemented using a host-aware embodiment in which acombination of a drive managed and host managed implementation isdeployed.

The storage systems described herein may be used to form a data lake. Adata lake may operate as the first place that an organization's dataflows to, where such data may be in a raw format. Metadata tagging maybe implemented to facilitate searches of data elements in the data lake,especially in embodiments where the data lake contains multiple storesof data, in formats not easily accessible or readable (e.g.,unstructured data, semi-structured data, structured data). From the datalake, data may go downstream to a data warehouse where data may bestored in a more processed, packaged, and consumable format. The storagesystems described above may also be used to implement such a datawarehouse. In addition, a data mart or data hub may allow for data thatis even more easily consumed, where the storage systems described abovemay also be used to provide the underlying storage resources necessaryfor a data mart or data hub. In embodiments, queries the data lake mayrequire a schema-on-read approach, where data is applied to a plan orschema as it is pulled out of a stored location, rather than as it goesinto the stored location.

The storage systems described herein may also be configured implement arecovery point objective (‘RPO’), which may be establish by a user,established by an administrator, established as a system default,established as part of a storage class or service that the storagesystem is participating in the delivery of, or in some other way. A“recovery point objective” is a goal for the maximum time differencebetween the last update to a source dataset and the last recoverablereplicated dataset update that would be correctly recoverable, given areason to do so, from a continuously or frequently updated copy of thesource dataset. An update is correctly recoverable if it properly takesinto account all updates that were processed on the source dataset priorto the last recoverable replicated dataset update.

In synchronous replication, the RPO would be zero, meaning that undernormal operation, all completed updates on the source dataset should bepresent and correctly recoverable on the copy dataset. In best effortnearly synchronous replication, the RPO can be as low as a few seconds.In snapshot-based replication, the RPO can be roughly calculated as theinterval between snapshots plus the time to transfer the modificationsbetween a previous already transferred snapshot and the most recentto-be-replicated snapshot.

If updates accumulate faster than they are replicated, then an RPO canbe missed. If more data to be replicated accumulates between twosnapshots, for snapshot-based replication, than can be replicatedbetween taking the snapshot and replicating that snapshot's cumulativeupdates to the copy, then the RPO can be missed. If, again insnapshot-based replication, data to be replicated accumulates at afaster rate than could be transferred in the time between subsequentsnapshots, then replication can start to fall further behind which canextend the miss between the expected recovery point objective and theactual recovery point that is represented by the last correctlyreplicated update.

The storage systems described above may also be part of a shared nothingstorage cluster. In a shared nothing storage cluster, each node of thecluster has local storage and communicates with other nodes in thecluster through networks, where the storage used by the cluster is (ingeneral) provided only by the storage connected to each individual node.A collection of nodes that are synchronously replicating a dataset maybe one example of a shared nothing storage cluster, as each storagesystem has local storage and communicates to other storage systemsthrough a network, where those storage systems do not (in general) usestorage from somewhere else that they share access to through some kindof interconnect. In contrast, some of the storage systems describedabove are themselves built as a shared-storage cluster, since there aredrive shelves that are shared by the paired controllers. Other storagesystems described above, however, are built as a shared nothing storagecluster, as all storage is local to a particular node (e.g., a blade)and all communication is through networks that link the compute nodestogether.

In other embodiments, other forms of a shared nothing storage clustercan include embodiments where any node in the cluster has a local copyof all storage they need, and where data is mirrored through asynchronous style of replication to other nodes in the cluster either toensure that the data isn't lost or because other nodes are also usingthat storage. In such an embodiment, if a new cluster node needs somedata, that data can be copied to the new node from other nodes that havecopies of the data.

In some embodiments, mirror-copy-based shared storage clusters may storemultiple copies of all the cluster's stored data, with each subset ofdata replicated to a particular set of nodes, and different subsets ofdata replicated to different sets of nodes. In some variations,embodiments may store all of the cluster's stored data in all nodes,whereas in other variations nodes may be divided up such that a firstset of nodes will all store the same set of data and a second, differentset of nodes will all store a different set of data.

Readers will appreciate that RAFT-based databases (e.g., etcd) mayoperate like shared-nothing storage clusters where all RAFT nodes storeall data. The amount of data stored in a RAFT cluster, however, may belimited so that extra copies don't consume too much storage. A containerserver cluster might also be able to replicate all data to all clusternodes, presuming the containers don't tend to be too large and theirbulk data (the data manipulated by the applications that run in thecontainers) is stored elsewhere such as in an S3 cluster or an externalfile server. In such an example, the container storage may be providedby the cluster directly through its shared-nothing storage model, withthose containers providing the images that form the executionenvironment for parts of an application or service.

For further explanation, FIG. 3D illustrates an exemplary computingdevice 350 that may be specifically configured to perform one or more ofthe processes described herein. As shown in FIG. 3D, computing device350 may include a communication interface 352, a processor 354, astorage device 356, and an input/output (“I/O”) module 358communicatively connected one to another via a communicationinfrastructure 360. While an exemplary computing device 350 is shown inFIG. 3D, the components illustrated in FIG. 3D are not intended to belimiting. Additional or alternative components may be used in otherembodiments. Components of computing device 350 shown in FIG. 3D willnow be described in additional detail.

Communication interface 352 may be configured to communicate with one ormore computing devices. Examples of communication interface 352 include,without limitation, a wired network interface (such as a networkinterface card), a wireless network interface (such as a wirelessnetwork interface card), a modem, an audio/video connection, and anyother suitable interface.

Processor 354 generally represents any type or form of processing unitcapable of processing data and/or interpreting, executing, and/ordirecting execution of one or more of the instructions, processes,and/or operations described herein. Processor 354 may perform operationsby executing computer-executable instructions 362 (e.g., an application,software, code, and/or other executable data instance) stored in storagedevice 356.

Storage device 356 may include one or more data storage media, devices,or configurations and may employ any type, form, and combination of datastorage media and/or device. For example, storage device 356 mayinclude, but is not limited to, any combination of the non-volatilemedia and/or volatile media described herein. Electronic data, includingdata described herein, may be temporarily and/or permanently stored instorage device 356. For example, data representative ofcomputer-executable instructions 362 configured to direct processor 354to perform any of the operations described herein may be stored withinstorage device 356. In some examples, data may be arranged in one ormore databases residing within storage device 356.

I/O module 358 may include one or more I/O modules configured to receiveuser input and provide user output. I/O module 358 may include anyhardware, firmware, software, or combination thereof supportive of inputand output capabilities. For example, I/O module 358 may includehardware and/or software for capturing user input, including, but notlimited to, a keyboard or keypad, a touchscreen component (e.g.,touchscreen display), a receiver (e.g., an RF or infrared receiver),motion sensors, and/or one or more input buttons.

I/O module 358 may include one or more devices for presenting output toa user, including, but not limited to, a graphics engine, a display(e.g., a display screen), one or more output drivers (e.g., displaydrivers), one or more audio speakers, and one or more audio drivers. Incertain embodiments, I/O module 358 is configured to provide graphicaldata to a display for presentation to a user. The graphical data may berepresentative of one or more graphical user interfaces and/or any othergraphical content as may serve a particular implementation. In someexamples, any of the systems, computing devices, and/or other componentsdescribed herein may be implemented by computing device 350.

For further explanation, FIG. 4 illustrates an example of a fleet ofstorage systems 376 for providing storage services (also referred toherein as ‘data services’). The fleet of storage systems 376 depicted inFIG. 3 includes a plurality of storage systems 374 a, 374 b, 374 c, 374d, 374 n that may each be similar to the storage systems describedherein. The storage systems 374 a, 374 b, 374 c, 374 d, 374 n in thefleet of storage systems 376 may be embodied as identical storagesystems or as different types of storage systems. For example, two ofthe storage systems 374 a, 374 n depicted in FIG. 4 are depicted asbeing cloud-based storage systems, as the resources that collectivelyform each of the storage systems 374 a, 374 n are provided by distinctcloud services providers 370, 372. For example, the first cloud servicesprovider 370 may be Amazon AWS™ whereas the second cloud servicesprovider 372 is Microsoft Azure™, although in other embodiments one ormore public clouds, private clouds, or combinations thereof may be usedto provide the underlying resources that are used to form a particularstorage system in the fleet of storage systems 376.

The example depicted in FIG. 4 includes an edge management service 366for delivering storage services in accordance with some embodiments ofthe present disclosure. The storage services (also referred to herein as‘data services’) that are delivered may include, for example, servicesto provide a certain amount of storage to a consumer, services toprovide storage to a consumer in accordance with a predetermined servicelevel agreement, services to provide storage to a consumer in accordancewith predetermined regulatory requirements, and many others.

The edge management service 366 depicted in FIG. 4 may be embodied, forexample, as one or more modules of computer program instructionsexecuting on computer hardware such as one or more computer processors.Alternatively, the edge management service 366 may be embodied as one ormore modules of computer program instructions executing on a virtualizedexecution environment such as one or more virtual machines, in one ormore containers, or in some other way. In other embodiments, the edgemanagement service 366 may be embodied as a combination of theembodiments described above, including embodiments where the one or moremodules of computer program instructions that are included in the edgemanagement service 366 are distributed across multiple physical orvirtual execution environments.

The edge management service 366 may operate as a gateway for providingstorage services to storage consumers, where the storage servicesleverage storage offered by one or more storage systems 374 a, 374 b,374 c, 374 d, 374 n. For example, the edge management service 366 may beconfigured to provide storage services to host devices 378 a, 378 b, 378c, 378 d, 378 n that are executing one or more applications that consumethe storage services. In such an example, the edge management service366 may operate as a gateway between the host devices 378 a, 378 b, 378c, 378 d, 378 n and the storage systems 374 a, 374 b, 374 c, 374 d, 374n, rather than requiring that the host devices 378 a, 378 b, 378 c, 378d, 378 n directly access the storage systems 374 a, 374 b, 374 c, 374 d,374 n.

The edge management service 366 of FIG. 4 exposes a storage servicesmodule 364 to the host devices 378 a, 378 b, 378 c, 378 d, 378 n of FIG.4, although in other embodiments the edge management service 366 mayexpose the storage services module 364 to other consumers of the variousstorage services. The various storage services may be presented toconsumers via one or more user interfaces, via one or more APIs, orthrough some other mechanism provided by the storage services module364. As such, the storage services module 364 depicted in FIG. 4 may beembodied as one or more modules of computer program instructionsexecuting on physical hardware, on a virtualized execution environment,or combinations thereof, where executing such modules causes enables aconsumer of storage services to be offered, select, and access thevarious storage services.

The edge management service 366 of FIG. 4 also includes a systemmanagement services module 368. The system management services module368 of FIG. 4 includes one or more modules of computer programinstructions that, when executed, perform various operations incoordination with the storage systems 374 a, 374 b, 374 c, 374 d, 374 nto provide storage services to the host devices 378 a, 378 b, 378 c, 378d, 378 n. The system management services module 368 may be configured,for example, to perform tasks such as provisioning storage resourcesfrom the storage systems 374 a, 374 b, 374 c, 374 d, 374 n via one ormore APIs exposed by the storage systems 374 a, 374 b, 374 c, 374 d, 374n, migrating datasets or workloads amongst the storage systems 374 a,374 b, 374 c, 374 d, 374 n via one or more APIs exposed by the storagesystems 374 a, 374 b, 374 c, 374 d, 374 n, setting one or more tunableparameters (i.e., one or more configurable settings) on the storagesystems 374 a, 374 b, 374 c, 374 d, 374 n via one or more APIs exposedby the storage systems 374 a, 374 b, 374 c, 374 d, 374 n, and so on. Forexample, many of the services described below relate to embodimentswhere the storage systems 374 a, 374 b, 374 c, 374 d, 374 n areconfigured to operate in some way. In such examples, the systemmanagement services module 368 may be responsible for using APIs (orsome other mechanism) provided by the storage systems 374 a, 374 b, 374c, 374 d, 374 n to configure the storage systems 374 a, 374 b, 374 c,374 d, 374 n to operate in the ways described below.

In addition to configuring the storage systems 374 a, 374 b, 374 c, 374d, 374 n, the edge management service 366 itself may be configured toperform various tasks required to provide the various storage services.Consider an example in which the storage service includes a servicethat, when selected and applied, causes personally identifiableinformation (PIP) contained in a dataset to be obfuscated when thedataset is accessed. In such an example, the storage systems 374 a, 374b, 374 c, 374 d, 374 n may be configured to obfuscate PII when servicingread requests directed to the dataset. Alternatively, the storagesystems 374 a, 374 b, 374 c, 374 d, 374 n may service reads by returningdata that includes the PII, but the edge management service 366 itselfmay obfuscate the PII as the data is passed through the edge managementservice 366 on its way from the storage systems 374 a, 374 b, 374 c, 374d, 374 n to the host devices 378 a, 378 b, 378 c, 378 d, 378 n.

The storage systems 374 a, 374 b, 374 c, 374 d, 374 n depicted in FIG. 4may be embodied as one or more of the storage systems described abovewith reference to FIGS. 1A-3D, including variations thereof In fact, thestorage systems 374 a, 374 b, 374 c, 374 d, 374 n may serve as a pool ofstorage resources where the individual components in that pool havedifferent performance characteristics, different storagecharacteristics, and so on. For example, one of the storage systems 374a may be a cloud-based storage system, another storage system 374 b maybe a storage system that provides block storage, another storage system374 c may be a storage system that provides file storage, anotherstorage system 374 d may be a relatively high-performance storage systemwhile another storage system 374 n may be a relatively low-performancestorage system, and so on. In alternative embodiments, only a singlestorage system may be present.

The storage systems 374 a, 374 b, 374 c, 374 d, 374 n depicted in FIG. 4may also be organized into different failure domains so that the failureof one storage system 374 a should be totally unrelated to the failureof another storage system 374 b. For example, each of the storagesystems may receive power from independent power systems, each of thestorage systems may be coupled for data communications over independentdata communications networks, and so on. Furthermore, the storagesystems in a first failure domain may be accessed via a first gatewaywhereas storage systems in a second failure domain may be accessed via asecond gateway. For example, the first gateway may be a first instanceof the edge management service 366 and the second gateway may be asecond instance of the edge management service 366, includingembodiments where each instance is distinct, or each instance is part ofa distributed edge management service 366.

As an illustrative example of available storage services, storageservices may be presented to a user that are associated with differentlevels of data protection. For example, storage services may bepresented to the user that, when selected and enforced, guarantee theuser that data associated with that user will be protected such thatvarious recovery point objectives (‘RPO’) can be guaranteed. A firstavailable storage service may ensure, for example, that some datasetassociated with the user will be protected such that any data that ismore than 5 seconds old can be recovered in the event of a failure ofthe primary data store whereas a second available storage service mayensure that the dataset that is associated with the user will beprotected such that any data that is more than 5 minutes old can berecovered in the event of a failure of the primary data store.

An additional example of storage services that may be presented to auser, selected by a user, and ultimately applied to a dataset associatedwith the user can include one or more data compliance services. Suchdata compliance services may be embodied, for example, as services thatmay be provided to consumers (i.e., a user) the data compliance servicesto ensure that the user's datasets are managed in a way to adhere tovarious regulatory requirements. For example, one or more datacompliance services may be offered to a user to ensure that the user'sdatasets are managed in a way so as to adhere to the General DataProtection Regulation (‘GDPR’), one or data compliance services may beoffered to a user to ensure that the user's datasets are managed in away so as to adhere to the Sarbanes-Oxley Act of 2002 (‘SOX’), or one ormore data compliance services may be offered to a user to ensure thatthe user's datasets are managed in a way so as to adhere to some otherregulatory act. In addition, the one or more data compliance servicesmay be offered to a user to ensure that the user's datasets are managedin a way so as to adhere to some non-governmental guidance (e.g., toadhere to best practices for auditing purposes), the one or more datacompliance services may be offered to a user to ensure that the user'sdatasets are managed in a way so as to adhere to a particular clients ororganizations requirements, and so on.

In order to provide this particular data compliance service, the datacompliance service may be presented to a user (e.g., via a GUI) andselected by the user. In response to receiving the selection of theparticular data compliance service, one or more storage servicespolicies may be applied to a dataset associated with the user to carryout the particular data compliance service. For example, a storageservices policy may be applied requiring that the dataset be encryptedprior to be stored in a storage system, prior to being stored in a cloudenvironment, or prior to being stored elsewhere. In order to enforcethis policy, a requirement may be enforced not only requiring that thedataset be encrypted when stored, but a requirement may be put in placerequiring that the dataset be encrypted prior to transmitting thedataset (e.g., sending the dataset to another party). In such anexample, a storage services policy may also be put in place requiringthat any encryption keys used to encrypt the dataset are not stored onthe same system that stores the dataset itself. Readers will appreciatethat many other forms of data compliance services may be offered andimplemented in accordance with embodiments of the present disclosure.

The storage systems 374 a, 374 b, 374 c, 374 d, 374 n in the fleet ofstorage systems 376 may be managed collectively, for example, by one ormore fleet management modules. The fleet management modules may be partof or separate from the system management services module 368 depictedin FIG. 4. The fleet management modules may perform tasks such asmonitoring the health of each storage system in the fleet, initiatingupdates or upgrades on one or more storage systems in the fleet,migrating workloads for loading balancing or other performance purposes,and many other tasks. As such, and for many other reasons, the storagesystems 374 a, 374 b, 374 c, 374 d, 374 n may be coupled to each othervia one or more data communications links in order to exchange databetween the storage systems 374 a, 374 b, 374 c, 374 d, 374 n.

In some embodiments, one or more storage systems or one or more elementsof storage systems (e.g., features, services, operations, components,etc. of storage systems), such as any of the illustrative storagesystems or storage system elements described herein may be implementedin one or more container systems. A container system may include anysystem that supports execution of one or more containerized applicationsor services. Such a service may be software deployed as infrastructurefor building applications, for operating a run-time environment, and/oras infrastructure for other services. In the discussion that follows,descriptions of containerized applications generally apply tocontainerized services as well.

A container may combine one or more elements of a containerized softwareapplication together with a runtime environment for operating thoseelements of the software application bundled into a single image. Forexample, each such container of a containerized application may includeexecutable code of the software application and various dependencies,libraries, and/or other components, together with network configurationsand configured access to additional resources, used by the elements ofthe software application within the particular container in order toenable operation of those elements. A containerized application can berepresented as a collection of such containers that together representall the elements of the application combined with the various run-timeenvironments needed for all those elements to run. As a result, thecontainerized application may be abstracted away from host operatingsystems as a combined collection of lightweight and portable packagesand configurations, where the containerized application may be uniformlydeployed and consistently executed in different computing environmentsthat use different container-compatible operating systems or differentinfrastructures. In some embodiments, a containerized application sharesa kernel with a host computer system and executes as an isolatedenvironment (an isolated collection of files and directories, processes,system and network resources, and configured access to additionalresources and capabilities) that is isolated by an operating system of ahost system in conjunction with a container management framework. Whenexecuted, a containerized application may provide one or morecontainerized workloads and/or services.

The container system may include and/or utilize a cluster of nodes. Forexample, the container system may be configured to manage deployment andexecution of containerized applications on one or more nodes in acluster. The containerized applications may utilize resources of thenodes, such as memory, processing and/or storage resources providedand/or accessed by the nodes. The storage resources may include any ofthe illustrative storage resources described herein and may includeon-node resources such as a local tree of files and directories,off-node resources such as external networked file systems, databases orobject stores, or both on-node and off-node resources. Access toadditional resources and capabilities that could be configured forcontainers of a containerized application could include specializedcomputation capabilities such as GPUs and AI/ML engines, or specializedhardware such as sensors and cameras.

In some embodiments, the container system may include a containerorchestration system (which may also be referred to as a containerorchestrator, a container orchestration platform, etc.) designed to makeit reasonably simple and for many use cases automated to deploy, scale,and manage containerized applications. In some embodiments, thecontainer system may include a storage management system configured toprovision and manage storage resources (e.g., virtual volumes) forprivate or shared use by cluster nodes and/or containers ofcontainerized applications.

FIG. 5 illustrates an example container system 380. In this example, thecontainer system 380 includes a container storage system 381 that may beconfigured to perform one or more storage management operations toorganize, provision, and manage storage resources for use by one or morecontainerized applications 382-1 through 382-L of container system 380.In particular, the container storage system 381 may organize storageresources into one or more storage pools 383 of storage resources foruse by containerized applications 382-1 through 382-L. The containerstorage system may itself be implemented as a containerized service.

The container system 380 may include or be implemented by one or morecontainer orchestration systems, including Kubernetes™, Mesos™, DockerSwarm™, among others. The container orchestration system may manage thecontainer system 380 running on a cluster 384 through servicesimplemented by a control node, depicted as 385, and may further managethe container storage system or the relationship between individualcontainers and their storage, memory and CPU limits, networking, andtheir access to additional resources or services.

A control plane of the container system 380 may implement services thatinclude: deploying applications via a controller 386, monitoringapplications via the controller 386, providing an interface via an APIserver 387, and scheduling deployments via scheduler 388. In thisexample, controller 386, scheduler 388, API server 387, and containerstorage system 381 are implemented on a single node, node 385. In otherexamples, for resiliency, the control plane may be implemented bymultiple, redundant nodes, where if a node that is providing managementservices for the container system 380 fails, then another, redundantnode may provide management services for the cluster 384.

A data plane of the container system 380 may include a set of nodes thatprovides container runtimes for executing containerized applications. Anindividual node within the cluster 384 may execute a container runtime,such as Docker™, and execute a container manager, or node agent, such asa kubelet in Kubernetes (not depicted) that communicates with thecontrol plane via a local network-connected agent (sometimes called aproxy), such as an agent 389. The agent 389 may route network traffic toand from containers using, for example, Internet Protocol (IP) portnumbers. For example, a containerized application may request a storageclass from the control plane, where the request is handled by thecontainer manager, and the container manager communicates the request tothe control plane using the agent 389.

Cluster 384 may include a set of nodes that run containers for managedcontainerized applications. A node may be a virtual or physical machine.A node may be a host system.

The container storage system 381 may orchestrate storage resources toprovide storage to the container system 380. For example, the containerstorage system 381 may provide persistent storage to containerizedapplications 382-1-382-L using the storage pool 383. The containerstorage system 381 may itself be deployed as a containerized applicationby a container orchestration system.

For example, the container storage system 381 application may bedeployed within cluster 384 and perform management functions forproviding storage to the containerized applications 382. Managementfunctions may include determining one or more storage pools fromavailable storage resources, provisioning virtual volumes on one or morenodes, replicating data, responding to and recovering from host andnetwork faults, or handling storage operations. The storage pool 383 mayinclude storage resources from one or more local or remote sources,where the storage resources may be different types of storage,including, as examples, block storage, file storage, and object storage.

The container storage system 381 may also be deployed on a set of nodesfor which persistent storage may be provided by the containerorchestration system. In some examples, the container storage system 381may be deployed on all nodes in a cluster 384 using, for example, aKubernetes DaemonSet. In this example, nodes 390-1 through 390-N providea container runtime where container storage system 381 executes. Inother examples, some, but not all nodes in a cluster may execute thecontainer storage system 381.

The container storage system 381 may handle storage on a node andcommunicate with the control plane of container system 380, to providedynamic volumes, including persistent volumes. A persistent volume maybe mounted on a node as a virtual volume, such as virtual volumes 391-1and 391-P. After a virtual volume 391 is mounted, containerizedapplications may request and use, or be otherwise configured to use,storage provided by the virtual volume 391. In this example, thecontainer storage system 381 may install a driver on a kernel of a node,where the driver handles storage operations directed to the virtualvolume. In this example, the driver may receive a storage operationdirected to a virtual volume, and in response, the driver may performthe storage operation on one or more storage resources within thestorage pool 383, possibly under direction from or using additionallogic within containers that implement the container storage system 381as a containerized service.

The container storage system 381 may, in response to being deployed as acontainerized service, determine available storage resources. Forexample, storage resources 392-1 through 392-M may include localstorage, remote storage (storage on a separate node in a cluster), orboth local and remote storage. Storage resources may also includestorage from external sources such as various combinations of blockstorage systems, file storage systems, and object storage systems. Thestorage resources 392-1 through 392-M may include any type(s) and/orconfiguration(s) of storage resources (e.g., any of the illustrativestorage resources described above), and the container storage system 381may be configured to determine the available storage resources in anysuitable way, including based on a configuration file. For example, aconfiguration file may specify account and authentication informationfor cloud-based object storage 348 or for a cloud-based storage system318. The container storage system 381 may also determine availability ofone or more storage devices 356 or one or more storage systems. Anaggregate amount of storage from one or more of storage device(s) 356,storage system(s), cloud-based storage system(s) 318, edge managementservices 366, cloud-based object storage 348, or any other storageresources, or any combination or sub-combination of such storageresources may be used to provide the storage pool 383. The storage pool383 is used to provision storage for the one or more virtual volumesmounted on one or more of the nodes 390 within cluster 384.

In some implementations, the container storage system 381 may createmultiple storage pools. For example, the container storage system 381may aggregate storage resources of a same type into an individualstorage pool. In this example, a storage type may be one of: a storagedevice 356, a storage array 102, a cloud-based storage system 318,storage via an edge management service 366, or a cloud-based objectstorage 348. Or it could be storage configured with a certain level ortype of redundancy or distribution, such as a particular combination ofstriping, mirroring, or erasure coding.

The container storage system 381 may execute within the cluster 384 as acontainerized container storage system service, where instances ofcontainers that implement elements of the containerized containerstorage system service may operate on different nodes within the cluster384. In this example, the containerized container storage system servicemay operate in conjunction with the container orchestration system ofthe container system 380 to handle storage operations, mount virtualvolumes to provide storage to a node, aggregate available storage into astorage pool 383, provision storage for a virtual volume from a storagepool 383, generate backup data, replicate data between nodes, clusters,environments, among other storage system operations. In some examples,the containerized container storage system service may provide storageservices across multiple clusters operating in distinct computingenvironments. For example, other storage system operations may includestorage system operations described herein. Persistent storage providedby the containerized container storage system service may be used toimplement stateful and/or resilient containerized applications.

The container storage system 381 may be configured to perform anysuitable storage operations of a storage system. For example, thecontainer storage system 381 may be configured to perform one or more ofthe illustrative storage management operations described herein tomanage storage resources used by the container system.

In some embodiments, one or more storage operations, including one ormore of the illustrative storage management operations described herein,may be containerized. For example, one or more storage operations may beimplemented as one or more containerized applications configured to beexecuted to perform the storage operation(s). Such containerized storageoperations may be executed in any suitable runtime environment to manageany storage system(s), including any of the illustrative storage systemsdescribed herein.

The storage systems described herein may support various forms of datareplication. For example, two or more of the storage systems maysynchronously replicate a dataset between each other. In synchronousreplication, distinct copies of a particular dataset may be maintainedby multiple storage systems, but all accesses (e.g., a read) of thedataset should yield consistent results regardless of which storagesystem the access was directed to. For example, a read directed to anyof the storage systems that are synchronously replicating the datasetshould return identical results. As such, while updates to the versionof the dataset need not occur at exactly the same time, precautions mustbe taken to ensure consistent accesses to the dataset. For example, ifan update (e.g., a write) that is directed to the dataset is received bya first storage system, the update may only be acknowledged as beingcompleted if all storage systems that are synchronously replicating thedataset have applied the update to their copies of the dataset. In suchan example, synchronous replication may be carried out through the useof I/O forwarding (e.g., a write received at a first storage system isforwarded to a second storage system), communications between thestorage systems (e.g., each storage system indicating that it hascompleted the update), or in other ways.

In other embodiments, a dataset may be replicated through the use ofcheckpoints. In checkpoint-based replication (also referred to as‘nearly synchronous replication’), a set of updates to a dataset (e.g.,one or more write operations directed to the dataset) may occur betweendifferent checkpoints, such that a dataset has been updated to aspecific checkpoint only if all updates to the dataset prior to thespecific checkpoint have been completed. Consider an example in which afirst storage system stores a live copy of a dataset that is beingaccessed by users of the dataset. In this example, assume that thedataset is being replicated from the first storage system to a secondstorage system using checkpoint-based replication. For example, thefirst storage system may send a first checkpoint (at time t=0) to thesecond storage system, followed by a first set of updates to thedataset, followed by a second checkpoint (at time t=1), followed by asecond set of updates to the dataset, followed by a third checkpoint (attime t=2). In such an example, if the second storage system hasperformed all updates in the first set of updates but has not yetperformed all updates in the second set of updates, the copy of thedataset that is stored on the second storage system may be up-to-dateuntil the second checkpoint. Alternatively, if the second storage systemhas performed all updates in both the first set of updates and thesecond set of updates, the copy of the dataset that is stored on thesecond storage system may be up-to-date until the third checkpoint.Readers will appreciate that various types of checkpoints may be used(e.g., metadata only checkpoints), checkpoints may be spread out basedon a variety of factors (e.g., time, number of operations, an RPOsetting), and so on.

In other embodiments, a dataset may be replicated through snapshot-basedreplication (also referred to as ‘asynchronous replication’). Insnapshot-based replication, snapshots of a dataset may be sent from areplication source such as a first storage system to a replicationtarget such as a second storage system. In such an embodiment, eachsnapshot may include the entire dataset or a subset of the dataset suchas, for example, only the portions of the dataset that have changedsince the last snapshot was sent from the replication source to thereplication target. Readers will appreciate that snapshots may be senton-demand, based on a policy that takes a variety of factors intoconsideration (e.g., time, number of operations, an RPO setting), or insome other way.

The storage systems described above may, either alone or in combination,by configured to serve as a continuous data protection store. Acontinuous data protection store is a feature of a storage system thatrecords updates to a dataset in such a way that consistent images ofprior contents of the dataset can be accessed with a low timegranularity (often on the order of seconds, or even less), andstretching back for a reasonable period of time (often hours or days).These allow access to very recent consistent points in time for thedataset, and also allow access to access to points in time for a datasetthat might have just preceded some event that, for example, caused partsof the dataset to be corrupted or otherwise lost, while retaining closeto the maximum number of updates that preceded that event. Conceptually,they are like a sequence of snapshots of a dataset taken very frequentlyand kept for a long period of time, though continuous data protectionstores are often implemented quite differently from snapshots. A storagesystem implementing a data continuous data protection store may furtherprovide a means of accessing these points in time, accessing one or moreof these points in time as snapshots or as cloned copies, or revertingthe dataset back to one of those recorded points in time.

Over time, to reduce overhead, some points in the time held in acontinuous data protection store can be merged with other nearby pointsin time, essentially deleting some of these points in time from thestore. This can reduce the capacity needed to store updates. It may alsobe possible to convert a limited number of these points in time intolonger duration snapshots. For example, such a store might keep a lowgranularity sequence of points in time stretching back a few hours fromthe present, with some points in time merged or deleted to reduceoverhead for up to an additional day. Stretching back in the pastfurther than that, some of these points in time could be converted tosnapshots representing consistent point-in-time images from only everyfew hours.

Although some embodiments are described largely in the context of astorage system, readers of skill in the art will recognize thatembodiments of the present disclosure may also take the form of acomputer program product disposed upon computer readable storage mediafor use with any suitable processing system. Such computer readablestorage media may be any storage medium for machine-readableinformation, including magnetic media, optical media, solid-state media,or other suitable media. Examples of such media include magnetic disksin hard drives or diskettes, compact disks for optical drives, magnetictape, and others as will occur to those of skill in the art. Personsskilled in the art will immediately recognize that any computer systemhaving suitable programming means will be capable of executing the stepsdescribed herein as embodied in a computer program product. Personsskilled in the art will recognize also that, although some of theembodiments described in this specification are oriented to softwareinstalled and executing on computer hardware, nevertheless, alternativeembodiments implemented as firmware or as hardware are well within thescope of the present disclosure.

In some examples, a non-transitory computer-readable medium storingcomputer-readable instructions may be provided in accordance with theprinciples described herein. The instructions, when executed by aprocessor of a computing device, may direct the processor and/orcomputing device to perform one or more operations, including one ormore of the operations described herein. Such instructions may be storedand/or transmitted using any of a variety of known computer-readablemedia.

A non-transitory computer-readable medium as referred to herein mayinclude any non-transitory storage medium that participates in providingdata (e.g., instructions) that may be read and/or executed by acomputing device (e.g., by a processor of a computing device). Forexample, a non-transitory computer-readable medium may include, but isnot limited to, any combination of non-volatile storage media and/orvolatile storage media. Exemplary non-volatile storage media include,but are not limited to, read-only memory, flash memory, a solid-statedrive, a magnetic storage device (e.g., a hard disk, a floppy disk,magnetic tape, etc.), ferroelectric random-access memory (“RAM”), and anoptical disc (e.g., a compact disc, a digital video disc, a Blu-raydisc, etc.). Exemplary volatile storage media include, but are notlimited to, RAM (e.g., dynamic RAM).

Migration of data from one failure domain, which may be referred to as awrite group, in a flash array involves considering a number of factors:

a) Space available in the entire array,

Blindly migrating data (in the presence of more incoming data) can fillthe array beyond comfortable limits, causing undue garbage collectionpressure and other performance anomalies.

b) Balancing space available in individual failure domains. As notedabove a failure domain may be referred to as a write group in someembodiments.

An imbalanced migration can cause some write groups to be fuller thanother write groups, causing performance hotspots for reads. Writehotspots can also be created due to the allocator preferring some writegroups over other.

c) Efficiency of migration.

Migrated data (depending on the method chosen) may migratedead/overwritten data in addition to live data.

The embodiments provide a write group space aware mechanism, write groupbalanced migration. Migration typically has two methods available:

RAID rebuild. This mechanism copies bits over, and includes live as wellas overwritten data. No space efficiency is achieved, however theoperation is faster, as it does not involve testing the data forliveness while migrating and the amount of data written out ispotentially smaller than in the GC move method. Since rebuild cannotchange the structure/geometry of segments, target write groups for thisdata can only be the same or larger write groups.

GC (garbage collection) move. This mechanism analyses the data, crossreferencing it with system metadata for check of liveness, and thenpicking up and recomposing the live-only data into new segments. This ismore space efficient as a GC move will only migrate live data, but ismore expensive in terms of central processing unit (CPU)/dynamic randomaccess memory (DRAM) resources. Since entire new segments are composed,the target write can be any (even smaller) write groups that are chosenby the system allocator. That is, deduplication is integrated into themigration mechanism with the inclusion of a GC move. It should beappreciated that while the embodiments include the above two migrationmechanisms, other migration techniques or mechanisms may be integratedwith the embodiments, as the embodiments are not limited to the abovetwo techniques.

In a system composed of write groups of different sizes, the migrationproceeds as follows:

1. Start off by using rebuild (favor performance over space efficiency).

2. As migration proceeds, compatible write groups will get fuller morequickly than incompatible write groups. A space monitoring modulesignals imbalance in space usage of the write groups.

3. Migration reacts to the imbalance signal, by switching some segmentmigration using GC. If the signal persists, a majority (up to 100%) ofthe migrated segments may move over to using the GC method.

4. At any time, if the space usage of the entire array approaches a setthreshold, migration just stalls, until the array is empty enough toproceed.

5. Also, at any time, GC method kicks in if space of the entire arraygoes above a soft threshold.

An aspect of this migration is that it considers side effects due tovarying incoming workloads automatically. An incoming workload can:

Help restore balance from imbalance: New writes are assigned theemptiest write groups by an allocator, helping to restore balanced writegroups.

Triggering space efficient migration by introducing imbalance: Suddenworkloads will cause an imbalance, causing migration to tilt towardsspace efficient GC, attempting to restore balance.

FIG. 6 is a block diagram of an embodiment of the space accounting logic301 shown in FIG. 3. Space accounting logic 301 could be implemented inhardware, firmware, software executing on a processor, or combinationthereof, in a storage system, for example a high-availabilitycontroller-based storage system, or in a storage cluster, for example ina storage node 150, or across storage nodes 150 as distributed softwareor logic. Further embodiments in various types of storage systems,including storage arrays in storage clusters, are readily devised inkeeping with the teachings herein. In some embodiments, each of thestorage nodes 150 of a storage cluster 160 has space accounting logic301. A space detector 602 monitors memory space, more specifically theamount of storage memory in blades, storage nodes 150 or storage units152, solid-state drives or other storage devices, etc., or the entiresystem, that is occupied by data or is available for writing data. Invarious embodiments, the space accounting logic 301 could count ordetermine erased blocks, written blocks, written pages or total amountof data written to a storage unit 152, for example using one or morecounters, score boarding, or other data structure and tracking erasuresand writing, or by directly detecting blocks that have no written data.In some embodiments, the space accounting logic 301 can track writegroups, for example by failure domain. For example, the space accountinglogic 301 could report on storage usage as a percentage of size of writegroups, space utilization from fullest to emptiest write group orfailure domain, or other aspects of storage space utilization andavailability.

Still referring to FIG. 6, a migration enable module 604 in the spaceaccounting logic 301 is coupled to the space detector 602, anddetermines whether migration should proceed with a RAID rebuild, asdirected by a RAID rebuild module 606, or by a garbage collection move,as directed by a garbage collection move module 608. This decision issymbolized in FIG. 6 by a switch, which could be implemented as asoftware decision, or in hardware or firmware.

FIG. 7 is an action diagram depicting migration from a storage unit 152,along with an incoming workload 704, directed by the space accountinglogic 301 and an allocator 702 in a storage node 150. In the scenarioshown here, the middle storage unit 152 is being evacuated, for exampleto free up the storage unit 152 for replacement or upgrade. Data isbeing migrated out of the storage unit 152, along a migration path 706to another storage unit 152. An alternate migration path 708 is shown indashed lines. Other evacuation and migration scenarios are readilydeveloped in keeping with the teachings herein.

As depicted in FIG. 7, the space accounting logic 301 monitors the spaceusage of multiple write groups or failure domains in the storage units152. This is performed using the space detector 602 shown in FIG. 6.Initially, the migration enable module 604 in the space accounting logic301 selects the RAID rebuild module 606, and directs a migration usingRAID rebuild. Based on the fullness of the compatible write groupsversus incompatible write groups, the migration can dynamically oradaptively switch to garbage collection move, as directed by themigration enable module 604 using the garbage collection move module608. For example, if the compatible write groups become more full thanthe incompatible write groups, the migration enable module 604 coulddirect a garbage collection move. This could be done with a percentageof the moves of the migration, or all of the moves of the migration, invarious embodiments. In some embodiments, the percentage of the moves ofthe migration that are directed to a garbage collection move is avariable percentage, based on the relative amount of fullness of thecompatible write groups in comparison with the fullness of theincompatible write groups. In some embodiments, there is a threshold,and all of the moves are switched from RAID rebuild to garbagecollection move once the threshold is exceeded. Also, if the space ofthe entire array is above a threshold, in some embodiments, themigration switches to garbage collection move. In some embodiments, ifthe space usage of the entire storage system reaches a threshold,migration is stalled until the array is empty enough to proceed (i.e.,the space usage drops below the threshold). In some embodiments,hysteresis is used for one or more thresholds.

Still referring to FIG. 7, the storage node 150 distributes writes fromthe incoming workload 704 in accordance with an allocator 702. Theallocator 702, in some embodiments, assigns new writes to the emptiestwrite groups, which helps to restore balanced write groups. During amigration, the system can switch back and forth between RAID rebuild andgarbage collection move for the migration, to restore or maintainbalance between compatible write groups and incompatible write groups.As noted above, other migration techniques may be integrated with theembodiments.

In one embodiment, the storage system uses remapping to changeaddressing and point future writes to the portion of storage space thathas more space available. This acts to rebalance the system bypreferentially filling up the newly added blade or storage unit, orrecently evacuated storage memory range with more available space, morerapidly than the blades, storage units, or other portions of storagememory with less storage space available. In various embodiments, theallocator 702 performs the remapping, or directs or otherwise cooperateswith a remapping module.

FIG. 8 is a flow diagram of a method for evacuating or migrating datafrom a write group. The method can be practiced by a storage node andstorage units of a storage cluster as described herein, in variousembodiments. Also, the method can be practiced by a processor in astorage system. In an action 802, a migration is started from a firstwrite group of a failure domain in a storage system to a second writegroup, using RAID rebuild. In an action 804, space usage of write groupsis monitored. In a determination action 806, it is determined whethercompatible write groups are more full than incompatible write groups. Infurther embodiments some other threshold could be applied to thisdetermination. If the answer in the determination action 806 is no, themigration is continued using RAID rebuild, in the action 808. If theanswer in the determination action 806 is yes, the migration is switchedto using garbage collection move, in the action 810.

Both of the actions 808, 810 rejoin the flow at the decision action 812,where it is determined whether the space usage of the storage system isbelow a threshold. If the answer to the determination action 812 is yes,the space usage is below threshold, flow continues with the action 816.If the answer to the decision action 812 is no, the space usage is notbelow the threshold (i.e., the space usage is at or above thethreshold), the migration is stalled in the action 814. Both of theactions 812, 814 rejoin the flow at the decision action 816, in which itis determined whether there are new writes from an incoming workload. Ifnot, flow proceeds back to the action 804, to continue monitoring spaceusage of write groups. If there are new writes from the incomingworkload, flow proceeds to the action 818, in which the allocatorassigns new writes to the emptiest write groups. Flow loops back to theaction 804, to continue monitoring space usage of write groups.

In variations of this flow, decision actions could be performed indifferent orders or combined. In a further variation, the portions ofthe migration could be continued using RAID rebuild while other portionscontinue using migration, proportional to or otherwise determined by aratio or comparison of the fullness of compatible write groups relativeto incompatible write groups. Thus, the mechanism can be employed in adynamic and/or adaptive manner in some embodiments. It should beappreciated that the embodiments through the inclusion of the garbagecollection move integrate and improve deduplication through theelimination of overwritten data.

It should be appreciated that garbage collection is used to distributedata evenly in a storage system, such as a storage array or storagecluster. Garbage collection involves reading live data and rewriting thedata, then reclaiming storage memory that has dead data by erasing thoseparts of storage memory. The act of rewriting the data, in garbagecollection, spreads the data throughout the storage system, for exampleas data stripes. Space pressure, based on detecting that the overallamount of available storage space is low, triggers garbage collection.Adding additional storage to alleviate space pressure, such as morestorage in a storage array or storage cluster, can create hotspots asnew data to be written is preferentially written to the added storage.Adding additional storage can also create a situation where the newstorage is underutilized since the data striping relies on segmentsdistributed across existing storage and the new storage, and theexisting storage isn't seen by the system as having enough extra spaceto support added data stripes that use existing storage and the newstorage. Adding the additional storage decreases space pressure, as thesystem detects that there is now more available storage space and doesnot trigger garbage collection. The two issues can be summarized as 1)adding additional storage doesn't necessarily improve system performanceand may actually decrease system performance with the production ofhotspots, and 2) adding additional storage doesn't necessarily increasetotal system storage space, since the added storage is underutilized insome storage systems. The new or additional storage space can be overutilized or underutilized, in either case creating problems.

One solution to the above mentioned issues is upon detecting addition ofstorage memory, e.g., insertion of a storage blade in a storage cluster,or added storage memory in a storage array, the system declares that theexisting storage memory is entirely full of candidate memory for garbagecollection, i.e., is full of garbage, and turns on garbage collection. Asecond solution is that the system tracks balance and imbalance ofportions of storage memory in terms of amount of storage memory usedversus amount of storage memory available, for example across blades,write groups, storage packs, solid-state drives, etc., upon detectingaddition of storage memory, for example insertion of a blade to astorage cluster or addition of a storage pack, storage group or storagedrive to a storage array, the system declares that imbalance hasoccurred, and triggers garbage collection. Garbage collection continuesuntil the imbalance is corrected, for example until storage memory isevenly filled to within a threshold. In both of the above mentionedsolutions, the storage system benefits from garbage collection, in termsof recovering storage memory space, benefits for data retention becausethe data is rewritten, and benefits by distributing data across a largerspan of storage memory, including the newly added storage memory.

With reference back to FIG. 6, there is a depiction of space accountinglogic 301, including a space detector 602, a migration enable module604, a RAID rebuild module 606, and a garbage collection move module608. The space accounting logic 301 or portions thereof can be used inimplementing one or both of the above solutions. In the first solution,the system could write to the space detector 602, for example with amessage or by writing to a parameter in memory, to declare that theexisting storage memory is full of garbage. The migration enable module604 responds to this declaration in the space detector 602, and switcheson the garbage collection move module 608. This performs garbagecollection over the entire existing storage memory, recovering memory touse along with the newly inserted storage blade 502. With this as abackground operation, regular reading and writing operations cancontinue without disruption.

With reference back to FIG. 7, the space accounting logic 301 isdepicted monitoring space usage, for example of write groups, or theentire array relative to a threshold, of one storage unit 152 versusothers, or even of a storage node 150 in comparison to other storagenodes 150. When imbalance is detected, the space accounting logic 301switches from RAID rebuilds to garbage collection move. In someembodiments, this is a variable amount of RAID rebuilds and garbagecollection move, which could be proportional to the amount of balance orimbalance in the system. This mechanism is useful in implementing thesecond solution, where addition of a blade 502 to the storage cluster160, or addition of a storage unit 152 or a storage pack, storage groupor storage drive causes the space accounting logic to detect imbalanceof storage memory used versus memory available. This triggers a datamigration using garbage collection move. In some embodiments, this isswitched back and forth between garbage collection move and RAIDrebuild, and in other embodiments this is varied between garbagecollection move and RAID rebuild.

FIG. 9 depicts the space detector 602 and the garbage collection movemodule 608 of FIG. 6, with two alternatives for garbage collection torebalance an imbalance across the storage memory in accordance with someembodiments. In these scenarios, the circled 1 shows an empty storagememory 906 in the storage system. For example, the empty storage memory906 has been added to the storage system, by adding a blade 502 or astorage unit 152, or a blade 502 or storage unit 152 has been evacuated.The circled 2 shows detection of an imbalance across the storage memory.The space detector 602 detects the imbalance, and the garbage collectionmove module 608 decides to perform garbage collection to migrate datafrom candidate storage memory 904 for garbage collection to the targetstorage memory, the empty storage memory 906. The circled 3 shows twoalternatives for how garbage collection can be performed. In oneversion, garbage collection reads live data and stripes the live dataacross the storage cluster or the storage array. This is followed bycircled 4, in which the storage memory is recovered from the dead dataand the erased portions of storage memory, for example by block erasesof the dead data and left behind live data that has already been copiedand migrated to the target memory. In the alternative version, garbagecollection makes a byte for byte copy from candidate storage memory 904to target storage memory. This is likewise followed by circled 4, torecover storage memory as above. It should be appreciated that bothversions of garbage collection migrate data from more full portions ofstorage memory, and recover storage memory there, to more empty portionsof storage memory, thereby rebalancing the storage memory.

FIG. 10 is a flow diagram of a method for elective garbage collection instorage memory in accordance with some embodiments. The method isperformed by embodiments of the storage system described herein, morespecifically by one or more processors in a storage system. In an action1002, storage space available is monitored. For example, spaceaccounting logic could monitor storage space in each of the storagenodes, storage units or blades in a storage cluster. In a determinationaction 1004, the question is asked, is there an imbalance? Storage spaceused or available in each of the storage nodes, storage units or bladesin a storage cluster could be compared, and if a difference from one ofthese to another, or from one to the group average, etc., meets athreshold, an imbalance could be declared. If there is no imbalance,flow branches back to the action 1002, in order to continue monitoringstorage space available. If there is an imbalance, flow branches to theaction 1006. In the action 1006, garbage collection is performed torebalance storage space available.

Garbage collection could copy live data only, or could perform a bytefor byte copy (i.e., copy live and dead data indiscriminately, one byteafter another), from a candidate storage memory to a target storagememory that has more storage space available than the candidate storagememory. Garbage collection recovers storage memory, erasing former livedata that has been copied from the candidate storage memory and migratedto the target storage memory and also erasing dead data, usually inblock erases. The combination of triggering garbage collection whenimbalance is detected, and targeting the migration of data with garbagecollection, rebalances the storage memory.

FIG. 11 depicts a storage system with spool insertion optimizing andstorage memory partitioning, in accordance with some embodiments. Thevarious mechanisms described herein provide optimizations for use ofspooling, use of communication bandwidth, memory space management ofspool region memory, memory space management of storage memory, and themutual dependencies among these mechanisms. General information aboutspools is provided below, followed by description of specific mechanismsthat are implementable in software executing on one or more processors,firmware, hardware, or combinations thereof for embodiments of systemsand storage systems.

A spool may be a temporary storage area within the computer's RAM thatcontains input or output data in some embodiments. When a job or processis initiated on a computing device, but cannot be run immediately, it isoften placed in a spool. This process is referred to as spooling and thespool holds the data until the appropriate device is ready to use thedata. SPOOL is an acronym for simultaneous peripheral operations on-lineand is associated with a buffering mechanism or a process in which datais temporarily held to be used and executed by a device, program or thesystem. Data is sent to and stored in memory or other volatile storageuntil the program or computer requests it for execution.

It should be appreciated that spooling works like a typical requestqueue where data, instructions and processes from multiple sources areaccumulated for execution later on. Generally, spooling is maintained ona computer's physical memory which acts to buffer inter-componentcommunication which may include I/O device-specific interrupts. Thespool is processed in FIFO (first in, first out) manner, i.e., spoolinsertions are made into the spool and whatever first instruction isthere at the front of the queue will be popped and executed in someembodiments. Spools, spoolers and spooling, combine buffering andqueuing. Spools may be useful in pipes, pipelines and parallelpipelining. Generally, a spool, spooler or spooling can be employed todirect spool insertions to a destination, which could be a program, aprocess, a device, a memory, etc. Each portion of data and/or metadatathat is written as one cohesive entry to the spool, and latercommunicated out of the spool to a destination as the same cohesiveentry, is called a spool insertion.

In the context of writing to storage memory and embodiments describedherein, a spool buffers and queues data and/or metadata, which is latersent (i.e., spooled) to a storage device for writing to storage memoryand/or execution by a storage processor, storage controller, processoror distributed processing as instruction(s), as appropriate andaccording to the information in the data and/or metadata. Spoolinsertions and spool usage are optimized for efficient use ofcommunication bandwidth, and efficient match to storage memorypartitioning and allocation, to improve storage efficiency in variousways, in various embodiments described herein. In accordance with theembodiments of the distributed storage system described above, the spoolmay refer to an authorities' view of a collection of memory resources onblades of the storage system. The memory may be Non-volatile randomaccess memory as described with regard to FIGS. 1-3. In this embodiment,the spool data is in memory and non-volatile storage devices. Inaddition, with regard to the partitioning described herein, the spoolpartition may be based on a data type, a data size, input/outputoperations per second (IOPS), etc. The optimizations described below maygenerate a large payload by combining multiple inserts, e.g., inembodiments for a metadata partition where writes are usually small andIOPS constrained. It should be further appreciated that the partitioningand other optimizations described herein enable spool space garbagecollection, compaction, and or defragmentation more efficient.

With reference to FIG. 11, a processing device, which could be one ormore processors 1102 or distributed processing (e.g., across nodes orblades of a system), uses various data structures 1104 for addresstranslation, and tracking various aspects of data, metadata and memoryallocation, etc. (see FIGS. 1-10 for examples of suitable informationfor data structures 1104). Particularly, the data structure(s) 1104 canbe used for tracking allocation of spool region memory 1108, allocationof storage memory 1114, allocation of spools 1110, identification,logical and physical location of portions of data 1122 and portions ofmetadata 1124, requests, commands, status, etc., in a storage systemembodiment.

In the storage system, data 1122 and metadata 1124 are input to a spoolinsertion optimizer 1106, which optimizes first spool insertions 1130towards a first spool insertion size 1126, and optimizes second spoolinsertions 1132 towards a second spool insertion size 1128. Furtherembodiments could have further spool insertion sizes towards which tooptimize further spool insertions. The first spool insertions 1130 andsecond spool insertions 1132 are communicated to and loaded into spools1110, which use a spool region memory 1108 of the system, for example inRAM, DRAM, NVRAM, etc., across nodes or blades of a system. From thespools 1110, the system communicates the first spool insertions 1130 andsecond spool insertions 1132 to the storage devices 1112, which writethe first spool insertions 1130 and second spool insertions 1132 intothe first partition 1116 and the second partition 1118 of storage memory1114. Processes for writing, reading and other aspects of storage memory1114 operation are executed by processor(s) 1120 of the storage devices1112, which could be storage processors, storage controllers, etc.

One set of optimizations that improves storage efficiency by leavingfewer holes in storage memory 1114 is to match the size of first spoolinsertions 1130 to size of portions of storage memory 1114 in the firstpartition 1116, and match the size of second spool insertions 1132 tosize of portions of storage memory 1114 in the second partition 1118.This is accomplished by making many, or even a majority of the firstspool insertions 1130, each have the same size (for example the samenumber of bytes) as the size of the portion of storage memory 1114 inthe first partition 1118 to which the spool insertion 1130 is written,avoiding empty, unused memory space (known as holes). Further, makingmany, or even a majority of the second spool insertions 1132, each havethe same size (for example the same number of bytes) as the size of theportion of storage memory 1114 in the second partition 1118 to which thespool insertion 1130 is written, avoiding empty, unused memory space orholes. The mechanism for doing so is to have the spool insertionoptimizer 1106 take data 1122 and metadata 1124 and compose first spoolinsertions 1130 and second spool insertions 1132 according to the firstspool insertion size 1126 and second spool insertion size 1128,respectively, as much as can be done under the operating constraints ofthe storage system at any given time. It should be understood that someof the first spool insertions 1130 and/or second spool insertions 1132will likely be of sizes other than the first spool insertion size 1126or second spool insertion size 1128, and that the intent is to optimizethe spool insertion sizes maximally with the understanding that this isnot always possible (see, e.g., a timer mechanism in FIG. 12).

FIG. 12 depicts an optimization of the storage system of FIG. 11 withspool insertion sizes devised to match storage memory allocation, inaccordance with an embodiment. Dynamic allocation 1218 establishes thefirst partition 1116 and second partition 1118 in storage memory 1114 ona dynamic basis, which the system adjusts throughout the lifespan of thestorage memory 1114, for example through allocation of allocation units1214, 1216 initially and after memory becomes available after deletions,erasures, and/or garbage collection. One set of optimizations is to makethe allocation units 1214 from the first partition 1116 of storagememory 1114 have a larger size (e.g., number of bytes), than allocationunits 1216 from the second partition 1118 of storage memory 1114.Allocation unit size is then matched to spool insertion size, forefficient use of storage memory space with fewer holes resulting in someembodiments. In this embodiment, spool insertions 1208 have a largerspool insertion size than spool insertions 1210, which have a smallerspool insertion size. The larger spool insertion size of the spoolinsertions 1208 matches the larger allocation unit size of theallocation units 1214 in the first partition 1116 of storage memory 1114in some embodiments. It should be appreciate that the smaller spoolinsertion size of the spool insertions 1210 matches the smallerallocation unit size of the allocation units 1216 from the secondpartition 1118 of the storage memory 1114 in some embodiments. Asconsidered above, it may be the case that not all of the spoolinsertions 1208, 1210 will be of the desired matching size to respectiveallocation units, but the more this is so, the more efficient will bethe use of storage memory space. In some embodiments, a timer 1204establishes a timeout 1206, notifying the spool insertion optimizer 1106that it is time to output a spool insertion 1208, 1210 even if anoptimization has not been achieved.

In FIG. 12, a specific embodiment of the storage system has the data1122 undergoing compression 1202 in route to the spool insertionoptimizer 1106, and the metadata 1124 presented without compression tothe spool insertion optimizer 1106. Spool insertions 1208 thus arecomposed of portions of compressed data, and spool insertions 1210 arethus composed of metadata. Part of this optimization is establishing thefirst partition 1116 of storage memory 1114 for data, more specificallycompressed data, and establishing the second partition 1118 of storagememory 1114 for metadata. Another version is establishing the firstpartition 1116 of storage memory 1114 for one or more larger spoolinsertion sizes, and the second partition 1116 of storage memory 1114for one or more smaller spool insertion sizes. Further variations forfurther numbers of partitions and spool insertion sizes are possible. Insome embodiments these partitions 1116, 1118 and the types of data ormetadata belonging therein are strictly defined, in other embodimentsthis is looser and more along the lines of establishing a preference orrecommendation, or perhaps even a threshold ratio of accordance andexceptions.

FIG. 13A depicts splitting a record 1302 to optimize towards a spoolinsertion size. In this example, the record 1302 could be data ormetadata, or both, and is of too large a size (e.g., too many bytes),i.e., is greater than the desired optimized spool insertion size. Therecord 1302 may be divided out or split into multiple records 1304A,1304B, 1304C, 1304D, the majority or all of which are of the desiredoptimized spool insertion size. That is, in one embodiment, the spoolinsertion optimizer 1106 (see FIG. 11) produces records 1304A, 1304B,1304C, 1304D that are optimized towards a specific spool insertion size,from the record 1302. Other numbers of multiple records, and sizes, arereadily understood for further examples.

FIG. 13B depicts combining records 1306A, 1306B, 1306C, 1306D tooptimize towards a spool insertion size. In this example the records1306A, 1306B, 1306C, 1306D could be data, metadata, or both, withhomogeneity or heterogeneity of type, and are each of too small a size(e.g., too few bytes), i.e., smaller than the desired optimized spoolinsertion size. So, the records 1306A, 1306B, 1306C, 1306D are combined,for example through concatenation, encoding or other suitable process,into one record 1308 that is of the desired optimized spool insertionsize. That is, in one embodiment, the spool insertion optimizer 1106produces one record 1308 that is optimized towards a specific spoolinsertion size, from the records 1306A, 1306B, 1306C, 1306D. Othernumbers of multiple records, and sizes, are readily understood forfurther examples.

FIG. 13C depicts combining commands 1310A, 1310B, 1310C to optimizetowards a spool insertion size. In this example three or some othernumber of commands could be the same size or different sizes (e.g.,numbers of bytes), each too small (e.g., too few bytes), i.e., smallerthan the desired optimized spool insertion size. Thus, the commands1310A, 1310B, 1310C are combined, similarly to the example in FIG. 13B,into one command 1312 that is of the desired optimized spool insertionsize. That is, in one embodiment, the spool insertion optimizer 1106produces one command 1312 that is optimized towards a specific spoolinsertion size, from the commands 1310A, 1310B, 1310C. Other numbers ofmultiple commands, and sizes, are readily understood for furtherexamples.

FIG. 13D depicts inlining 1318 to optimize towards a spool insertionsize. In this example the compressed block writing command 1314 is acommand to write a compressed block of data, and the commit command 1316is a command to commit the write. Inlining 1318 the compressed blockwriting command 1314 and the commit command 1316 produces one command1320, which could be an example of a delegated command because itexpresses two commands in one command 1320. Another example of adelegated command could use tokens and distributed transactions allcombined into one command 1320. In one embodiment, the spool insertionoptimizer 1106 produces a single command 1320 that is optimized towardsa specific spool insertion size, from a writing command and a commitcommand. Further examples of inlining, with further commands and data ormetadata or both, and further examples of delegated commands, arereadily understood.

FIG. 14 depicts a further example of inlining 1318 to optimize towards aspool insertion size. In this example, data 1122 and metadata 1124 areeach smaller than the desired optimized spool insertion size. Throughinlining 1318, the data 1122 and metadata 1124 are combined into arecord of data inline with metadata 1322 that is of the desiredoptimized spool insertion size. In a variation, the inlining of data1122 and metadata 1124 produces a record of data inline with metadata1322 that is smaller than the desired optimized spool insertion size,but which nonetheless results in a single communication instead of twocommunications, thus conserving or using more efficiently communicationbandwidth. It should be appreciated that communication bandwidth is usedmore efficiently twice, once for communication into a spool 1110, andonce for communication from a spool 1110 to a storage device 1112. Thisoptimization benefit applies also to the examples in FIGS. 13B, 13C and13D and variations thereof.

FIG. 14 illustrates a byte allocator 1406 in a spool insertion optimizer1106, accessing various buckets 1404 to compose spool insertions, inaccordance with an embodiment. Requests 1402 are written into buckets1404, for example as objects in buckets. For example, a request 1402 inthe form of a 512 byte object could be placed in a bucket 1404C that issized for a 512 byte object size, a request 1402 in the form of a 4 KBobject could be placed in a bucket 1404D that is sized for a 4 KB objectsize, further requests could be placed in other buckets 1404A, 1404Cwith other sizes, each object according to the size of the bucket. Theserequests could include data, metadata, or combinations, commands,commands combined with data or metadata, etc.

The byte allocator 1406 accesses the various buckets 1404, andcoordinates with the spool insertion optimizer 1106 to obtain requestsof various sizes and compose spool insertions 1408 that are optimizedtowards specified spool insertion size(s). For example, the byteallocator 1406 could obtain multiple requests from one of the buckets1404, for combination, multiple requests from multiple buckets 1404, forcombination, a single request from one of the buckets 1404 that is asize match to a specified spool insertion size, a single request fromone of the buckets 1404 that is larger than a specified spool insertionsize, but can be broken down to smaller sizes that either match aspecified spool insertion size or can be combined to do so, etc.

One embodiment of the buckets 1404 is that the buckets could includebuckets within buckets, i.e., nested buckets, allowing a larger bucket1404C to fill up either by a request 1402 written to that bucket 1404C,or requests 1402 written to buckets 1404B, 1404A within the bucket1404C, for example. Various combinations of separate buckets and nestedbuckets, even nested buckets within nested buckets, or multiple separatebuckets nested within a bucket, etc., are possible in embodiments.

FIG. 15 illustrates authorities 168, spool allocation 1502 and storagememory allocation 1504 for various optimizations for versions of thestorage systems of FIG. 11 and FIG. 12. Storage system embodiments withauthorities 168 are described above with reference to FIGS. 2B-2E, andvariations thereof are readily developed with the following features. InFIG. 15, each authority 168 is allocated multiple spools 1110, andoutputs of the spools 1110 are directed towards allocation units instorage memory 1114. The allocation units of storage memory 1114 areassigned to authorities 168 through storage memory allocation 1504,which is subjected to the partitioning previously described. In order tomatch a spool insertion size to an allocation unit size, each authority168 uses a specific spool 1110 both for spool insertions are optimizedto a specified spool insertion size, and for transferring spoolinsertions from the spool 1110 to allocation units from a specifiedpartition of the storage memory 1114. The following example applies toan embodiment in accordance with FIG. 15.

Authority 168A, labeled “Authority A”, uses a spool 1110 labeled “Spool1” for first spool insertions 1130 that are optimized towards the firstspool insertion size 116, and transfers such first spool insertions 1130from the Spool 1 to storage memory 1114 allocation units 1214 that havebeen allocated from the first partition 1116 and thus have an allocationunit size that matches the first spool insertion size 1126.

Authority 168A uses a spool 1110 labeled “Spool 2” for second spoolinsertions 1132 that are optimized towards the second spool insertionsize 1128 in some embodiments. Authority 168A transfers the second spoolinsertions 1132 from the Spool 2 to storage memory 1114 allocation units1216 that have been allocated from the second partition 1118 and thushave an allocation unit size that matches the second spool insertionsize 1128. It should be appreciated that there could be further spools1110, such as “spool 3”, which could use allocation units from the firstpartition 1116, the second partition 1118, or another partition, etc.Another authority 168B, labeled “Authority B” similarly has multiplespools 1110 and allocation of allocation units 1214, 1216 from the firstpartition 1116 and second partition 1118 of storage memory 1114. Furtherauthorities 168 have further spools 1110 and allocation from storagememory 1114.

This optimization makes efficient use of communication bandwidth, andefficient use of storage memory 1114, with parallel spooling from theauthorities 168. Dynamic allocation of spools 1110 from spool regionmemory 1108, and dynamic allocation of allocation units from thepartitions in storage memory 1114, supports system adjustment tochanging I/O conditions and also to changes in memory availability,e.g., through system maintenance, failure, replacement or upgrade. Itshould be appreciated that storage memory 1114 physical addressing doesnot necessarily match logical addressing, and the first partition 1116and second partition 1118 do not necessarily correspond to a singlephysical address separating the two partitions. That is, allocationunits from one or the other partition could be intermingled and adjacentto one another in physical address space. For example, in physicaladdress space, an allocation unit 1214 from the first partition 1116could be allocated to authority 168A and have a first spool insertion1130 written from that authority's spool 1. Adjacent in physical addressspace, an allocation unit 1216 from the second partition 1116 could beallocated to authority 168B and have a second spool insertion 1132written from that authority's spool 2. The next adjacency in physicaladdress space could have a spool insertion from some other authority168, some other spool 1110, etc., in keeping with dynamic allocation ofstorage memory, logical to physical address translation and trackingacross one or multiple layers of indirection, etc.

FIG. 16 illustrates a multi chassis storage system for variousoptimizations for versions of the storage systems of FIG. 11 and FIG.12. Two chassis are depicted for this example, but embodiments couldhave more chassis. Each chassis 1602A, 1602B has a fabric module (FM)1608 for communication across the blades 1604. Blades 1604 haveauthorities 168, spool region memory 1108 and storage memory 1114. Anexternal fabric module (XFM) handles communication among chassis 1602.Generally, the storage system, and particularly the authorities 168, canwrite error correction coded data stripes, e.g., RAID stripes, acrossblades 1604, across storage nodes in some embodiments, across storageunits in some embodiments, etc., in various combinations that caninclude components in one chassis or components in multiple chassis.Communication among blades 1604 in one chassis 1602A involvescommunication from a blade 1604 to the fabric module 1608 and to anotherblade 1604 in the same chassis 1602A, likewise for the chassis 1602B.Communication from a blade 1604 in one chassis 1602A to a blade 1604 inanother chassis 1602B involves communication from a blade 1604 to thefabric module 1608 in one chassis 1602A, from that fabric module 1608 tothe external fabric module 1606, from the external fabric module 1606 tothe fabric module 1608 of the other chassis 1602B, and from that fabricmodule 1608 to the blade 1604 in the other chassis 1602B. Clearly,inter-chassis communication involves extra communication hops,introducing extra latency and consuming communication bandwidth.

One optimization in multi-chassis embodiments is to establish preferencefor authorities 168 on one chassis 1602A of a multi-chassis storagesystem to use NVRAM or other spool region memory 1108 resources in theone chassis 1602A for the first spool insertions 1130 and the secondspool insertions 1132 of the authorities 168 on the one chassis 1602A.It should be appreciated that this avoids extra communication hops aswould occur if the authorities 168 of the chassis 1602A were usingspools defined or allocated in NVRAM or other spool region memory 1108in another chassis 1602B, and thus optimizes use of communicationbandwidth and optimizes space management of spool region memory. In someembodiments, authorities on a specific blade 1604 could use spool regionmemory 1108 of other blades 1604 in the same chassis 1602A, for exampleto have spools allocated and make spool insertions, while stillobserving the established preference to avoid using spool region memory1108 on another chassis 1602B.

FIG. 17 is a flow diagram of a method for memory space management, whichcan be practiced on or by embodiments described herein, variationsthereof, and further systems and storage systems. More specifically, themethod can be practiced by a processing device, such as in a storagesystem or system using storage memory. The method includes optimizationsfor memory space management of spool region memory, memory spacemanagement of storage memory, and use of communication bandwidth.

In an action 1702, the system performs dynamic allocation of storagememory, and storage devices, with a first partition and a secondpartition. In various embodiments, this includes allocation units of alarger size allocated from the first partition of storage memory, andallocation units of a smaller size allocated from the second partitionof storage memory. In an action 1704, the system allocates spools withspool region memory. In various embodiments, spools are allocated toauthorities in the system, and associated to specified spool insertionsizes that match specified allocation unit sizes from specifiedpartitions of storage memory.

In an action 1706, the system optimizes first spool insertions towards afirst spool insertion size. Examples are given above as to how suchoptimizing towards a specified spool insertion size can be accomplished.In some embodiments, the first spool insertions are composed of data, orcompressed data. In an action 1708, the system communicates first spoolinsertions into spools. In some embodiments, this is done by authoritiesin the system.

In an action 1710, the system communicates first spool insertions fromthe spools to storage devices, to write into the first partition ofstorage memory. By optimizing the first spool insertions towardsmatching the size of the allocation units from the first partition ofstorage memory, the system optimizes efficient use of storage memory, insome embodiments. In an action 1712, the system optimizes second spoolinsertions towards a second spool insertion size. In some embodiments,the second spool insertions are composed of metadata. In someembodiments, the second spool insertion size is smaller than the firstspool insertion size.

In an action 1714, the system communicates second spool insertions intospools. In some embodiments, this is done by authorities in the system.In an action 1716, the system communicates second spool insertions fromthe spools to the storage devices, to write into the second partition ofstorage memory. By optimizing the second spool insertions towardsmatching the size of the allocation units from the second partition ofstorage memory, the system optimizes efficient use of storage memory, insome embodiments.

FIG. 18 illustrates a storage system 1802 that allocates virtual unitsof memory 1814 and defragments NVRAM 1820, in accordance with anembodiment. As with other embodiments described herein, the variouscomponents can be implemented in software executing on a processor 1804,firmware (e.g., using processor 1804 or portion thereof), hardware, orcombination thereof, for example in hardware modules, firmware modules,software modules, electronic circuitry, etc. The processor 1804 can beimplemented with one or more processors, multiprocessing, distributedprocessing, virtual processing executed on physical components, or otherprocessing device, etc. Some embodiments have authorities 168 in thestorage system 1802, for example as illustrated in FIGS. 2B and 2E-2Gand 15, some do not. Authorities 168, in such embodiments, each own arange of data and handle owned data within that owned range of data.Some embodiments have various combinations of spools, byte allocators,objects and/or allocation units, as described with reference to FIGS. 7,12, 14 and 15.

In the storage system 1802, a memory manager 1806 manages NVRAM 1820,for staging 1822, and storage memory 1828, for transfer 1826 of datafrom NVRAM 1820 to storage memory 1828. An allocator 1808, hereindepicted as an integrated part of memory manager 1806 but could bedistinct in various embodiments, allocates virtual units of memory 1814for the storage system 1802 to use in a write 1816 process for varioustypes of data (e.g., broadly including metadata). In each write 1816,the storage system 1802, and more specifically in some embodimentsauthorities 168, writes data into one or more of various sizes ofallocated virtual units of memory 1814, herein illustrated assmaller-sized virtual units 1810 and larger-sized virtual units 1812(but which could include more and/or further variable sizes in variousembodiments). Mapping 1818 (e.g., implemented in a mapping module thataccesses and uses metadata, see FIG. 19) tracks the correspondencebetween the virtual units of memory 1814 and physical memory in NVRAM1820 and also physical memory in storage memory 1828 (e.g., solid-statestorage memory such as flash, or other memory types, in variousembodiments). Mapping 1818 cooperates with the allocator 1808, and couldbe integrated with the memory manager 1806, or distinct, in variousembodiments.

Following one scenario with reference to FIG. 18, an authority 168receives one or more virtual units of memory 1814 from the allocator1808 or more generally from the memory manager 1806, and performs awrite 1816 of data into a virtual unit of memory 1814 of appropriatesize for the write 1816. For example, the allocator 1808 could allocatevirtual units as allocation units, to authorities 168, each according tothe range of data owned by the authority 168. Using mapping 1818, thedata is written into a physical location in the NVRAM 1820 (e.g., usingphysical addressing, see FIG. 19). The NVRAM 1820 is used for staging1822, in which there is a subsequent transfer 1826 of the data from theNVRAM 1820, to the storage memory 1828. That is, NVRAM 1820 is used as astaging region for data written (e.g. as a foreground process) intoNVRAM 1820, for later (e.g., as a background process) transfer 1826 ofdata into storage memory 1828 in some embodiments. One or more stages ofwriting into and out of the various memories can involve appropriateformatting, data chunking, RAID stripe formation, replication,compression, encryption, deduplication, etc., as specific to a storagesystem 1802 embodiment.

For example, the storage system 1802 (or more specifically an authority168) could use a smaller-sized virtual unit(s) 1810, for example eachhaving a size of 512 bytes, for writing metadata, small, random-accessor low-throughput data, and use a larger-sized virtual unit(s) 1812, forexample each having a size of 4 kB (kilobytes) or 8096 bytes, forwriting larger, logically contiguous or high-throughput data. It shouldbe appreciated that having more than one size of virtual units of memory1814 affords the storage system 1802 versatility and efficiency of bothmemory usage and throughput for writes 1816, through sizing of virtualunits appropriate to data type or characteristics.

Supporting such versatility and efficiency, a defragmenter 1824defragments the NVRAM 1820, to free up contiguous space in the NVRAM1820. Operation of the defragmenter 1824, and contribution of contiguousspace in the NVRAM 1820 to efficiency and throughput in the storagesystem 1802, are further described below with reference to FIGS. 19 and20.

FIG. 19 illustrates physical memory 1902 and virtual units of memory1926, with mapping 1818, a device layer 1910 and a logical layer 1912,in an embodiment of a storage system. Through mapping 1818, usingmetadata 1906, the storage system 1802 tracks correspondence betweenphysical addressing 1904 of portions of NVRAM 1820 and logicaladdressing 1908 of virtual units of memory 1926. In some embodiments,the operating system 1914 of the storage system 1802 (e.g., acentralized operating system in some embodiments, a distributedoperating system in some embodiments) has a logical layer 1912 in whichlogical addressing 1908 is used, and a device layer 1910 in whichphysical addressing 1904 is used, for referencing various types ofmemory. Mapping 1818 translates physical to logical and vice versa.

In the embodiment in FIG. 19, physical memory 1902 includes NVRAM 1820with storage system support for writing at two different sizes ofphysical addressing 1904, for example a 512 byte write (i.e., a write to512 bytes of memory with contiguous physical addressing 1904), and a 4kilobyte write (i.e., a write to 4 kB of memory with contiguous physicaladdressing 1904). Further embodiments could have other numbers of and/ordifferent sizes of writes to portions of physical memory 1902 withcontiguous physical addressing 1904. Continuing in FIG. 19, multiplephysical NVRAM portions 1916, each composed of 512 bytes that arecontiguous in physical addressing 1904, are each mapped to acorresponding virtual unit 1928 of size A 1930, also 512 bytes in thisexample. Size A 1930 is for the smaller virtual units of memory 1926,illustrated as smaller-sized virtual units 1810 in FIG. 18. The physicalNVRAM portions 1916 can come from various locations in physical addressspace of NVRAM 1820, and do not need to be contiguous with one anotherin physical address space. That is, the physical addressing 1904 of thebytes in a given physical NVRAM portion 1916, mapped to a given virtualunit 1928 of size A, is contiguous, but the physical addressing 1904 ofone NVRAM portion 1916 mapped to a virtual unit 1928 does not need to becontiguous with physical addressing 1904 of another NVRAM portion 1916mapped to another virtual unit 1928.

For the larger virtual units of memory 1926, illustrated as larger-sizedvirtual units 1812 in FIG. 18, multiple physical NVRAM portions 1918,each composed of 512 bytes that are contiguous in physical addressing1904 for that NVRAM portion 1918, are mapped to a corresponding virtualunit 1932 of size B 1934, which is in this example is 4 kilobytes or4096 bytes. There is great flexibility with locations in physical memory1902 and NVRAM 1820 from which portions can be pulled together to map toa virtual unit 1932 of size B 1934, in various embodiments. For example,some or all of the physical NVRAM portions 1918 mapped to a virtual unit1932 can be at separate, non-contiguous locations, or two or more of thephysical NVRAM portions 1918 can be in contiguous locations in physicaladdressing 1904 as shown in the physical NVRAM portion 1920 composed oftwo NVRAM portions 1918. Further, all of the physical NVRAM portions1918 can be in contiguous locations in physical addressing 1904 as shownin the physical NVRAM portion 1922, as mapped to a virtual unit 1932 ofsize B 1908. This flexibility of mapping and composition of physicalmemory 1902 in virtual units of memory 1926 contributes to readyavailability of virtual units of memory 1926 for allocation, for thesmaller and the larger size virtual units of memory 1926.

FIG. 20 illustrates the defragmenter 1824 of FIG. 18 relocating data inthe NVRAM 1820, to free up contiguous space in the NVRAM 1820, inaccordance with an embodiment. In this example, one NVRAM portion 2002has two NVRAM portions 2006, 2008 that each have data therein and isthus presently not available for mapping to a virtual unit 1932 of sizeB 1934. The defragmenter 1824 moves data in one NVRAM portion 2006, toan unoccupied NVRAM portion 2018, and moves data of another NVRAMportion 2008 to another unoccupied NVRAM portion 2016, after which theNVRAM portion 2002 is unoccupied by data and available for mapping to avirtual unit 1932 of size B 1934 and allocation by the allocator 1808(see FIG. 18). To free up contiguous space in physical NVRAM portion2004, the defragmenter 1824 moves data in one NVRAM portion 2010 toanother unoccupied NVRAM portion 2020, moves data in one NVRAM portion2012 to another unoccupied NVRAM portion 2024, and moves data from NVRAMportion 2014 to another unoccupied NVRAM portion 2022. Then, thephysical NVRAM portion 2004 is available for mapping to a virtual unit1932 of size B 1934 and allocation by the allocator 1808. Furtherexamples with other amounts of data to move, other types of data to move(e.g., data not associated with virtual units), other sizes of virtualunits, other sizes of portions of NVRAM, etc., are readily devised inkeeping with the teachings herein for further embodiments.

Defragmenting the NVRAM 1820 in this manner is advantageous in a storagesystem where writing data in amount of size B 1934, for example 4 kB, toa contiguous physical addressed amount of NVRAM 1820, for example NVRAMportions 1922, 2002, 2004, is more efficient than writing the sameamount of data across multiple non-contiguous physical addressedportions of NVRAM, for example NVRAM portions 1918 at disparatelocations in physical addressing 1904. Using example sizes describedherein, a 4 kB write performed as a single write operation to a 4 kBcontiguous physical addressed portion of NVRAM is more efficient than a4 kB write performed as eight write operations each to a 512 bytecontiguous physical addressed portion of NVRAM. The teachings herein areapplicable to other sizes, other numbers of different sizes for writes,and other types of staging memory.

FIG. 21 is a flow diagram of a method of operating a storage system withvirtual units of memory, which can be practiced on or by embodimentsdescribed herein and further storage system embodiments. Morespecifically, the method can be practiced by a processor in a storagesystem. The method can be embodied in tangible, non-transitory,computer-readable media that has instructions for execution by aprocessor.

In an action 2102, the storage system allocates virtual units of NVRAM,with mapping to physical memory. The virtual units have greater than orequal to two sizes, i.e., more than one size. In some embodiments, thereare two sizes, larger and smaller virtual units corresponding to ormapped to larger and smaller amounts of physical NVRAM as describedabove. Allocating more than one size of virtual units contributes tostorage system versatility and efficiency.

In an action 2104, the storage system writes data to allocated virtualunits. Through the mapping to physical memory, the data is written tophysical NVRAM, i.e., portions of NVRAM with physical addressing, e.g.,through the mapping module described with reference to FIGS. 18-20.

In an action 2106, the storage system defragments NVRAM, to makecontiguous physical addressed amounts of NVRAM available for allocatinglarger-sized virtual units. It should be appreciated that thedefragmentation concatenates/relocates portions of data in order to makeavailable larger contiguous portions of space in the NVRAM, which inturn contributes to storage system efficiency and throughput.

Advantages and features of the present disclosure can be furtherdescribed by the following statements:

1. A method, comprising:

allocating, by a memory manager in a storage system, virtual units ofNVRAM (nonvolatile random-access memory) with mapping of the virtualunits to physical memory;

writing data having various sizes into allocated first virtual units ofmemory each comprising a first contiguous physical addressed amount ofNVRAM having a first size, and into allocated second virtual units ofmemory each comprising an amount of NVRAM having a second, larger size,as staging for later transfer of data from NVRAM to solid-state storagememory of the storage system; and

defragmenting the NVRAM, to have available for the allocating, aplurality of the second virtual units of memory each comprising acontiguous physical addressed amount of NVRAM having the second, largersize.

2. The method of claim 1, wherein the defragmenting comprises:

relocating data in the NVRAM corresponding to a plurality of the firstvirtual units of memory, to free up contiguous space in the NVRAM.

3. The method of claim 1, wherein the defragmenting comprises:

relocating data in the NVRAM, to free up a plurality of contiguousspaces in the NVRAM, each comprising the contiguous physical addressedamount of NVRAM having the second, larger size.

4. The method of claim 1, wherein the defragmenting comprises:

relocating data in the NVRAM corresponding to portions of the secondvirtual units of memory, to free up contiguous space in the NVRAM.

5. The method of claim 1, wherein the first contiguous physicaladdressed amount of NVRAM having the first size comprises 512 bytes ofNVRAM having contiguous physical addressing, the amount of NVRAM havingthe second, larger size comprises eight portions of NVRAM each having512 bytes of NVRAM having contiguous physical addressing totaling 4 kB(kilobytes) of NVRAM, and the contiguous physical addressed amount ofNVRAM having the second, larger size comprises 4 kB of NVRAM havingcontiguous physical addressing.

6. The method of claim 1, wherein the writing data into allocated firstvirtual units of memory and allocated second virtual units of memory isperformed by a plurality of authorities in the storage system, eachauthority owning a range of data and handling data in the range of datausing first virtual units of memory and second virtual units of memoryallocated to the authority by the memory manager.

7. The method of claim 1, wherein the writing operates at a logicallayer in the storage system, the defragmenting operates at a devicelayer, in the storage system, and the mapping translates between thelogical layer and the device layer.

8. A tangible, non-transitory, computer-readable media havinginstructions thereupon which, when executed by a processor, cause theprocessor to perform a method comprising:

allocating, by a memory manager in a storage system, virtual units ofNVRAM (nonvolatile random-access memory) with mapping of the virtualunits to physical memory;

writing data having various sizes into allocated first virtual units ofmemory each comprising a first contiguous physical addressed amount ofNVRAM having a first size, and into allocated second virtual units ofmemory each comprising an amount of NVRAM having a second, larger size,as staging for later transfer of data from NVRAM to solid-state storagememory of the storage system; and

defragmenting the NVRAM, to have available for the allocating, aplurality of the second virtual units of memory each comprising acontiguous physical addressed amount of NVRAM having the second, largersize.

9. The computer-readable media of claim 8, wherein the defragmentingcomprises:

relocating data in the NVRAM corresponding to a plurality of the firstvirtual units of memory, to free up contiguous space in the NVRAM.

10. The computer-readable media of claim 8, wherein the defragmentingcomprises:

relocating data in the NVRAM, to free up a plurality of contiguousspaces in the NVRAM, each comprising the contiguous physical addressedamount of NVRAM having the second, larger size.

11. The computer-readable media of claim 8, wherein the defragmentingcomprises:

relocating data in the NVRAM corresponding to portions of the secondvirtual units of memory, to free up contiguous space in the NVRAM.

12. The computer-readable media of claim 8, wherein the first contiguousphysical addressed amount of NVRAM having the first size comprises 512bytes of NVRAM having contiguous physical addressing, the amount ofNVRAM having the second, larger size comprises eight portions of NVRAMeach having 512 bytes of NVRAM having contiguous physical addressingtotaling 4 kB (kilobytes) of NVRAM, and the contiguous physicaladdressed amount of NVRAM having the second, larger size comprises 4 kBof NVRAM having contiguous physical addressing.

13. The computer-readable media of claim 8, wherein the writing datainto allocated first virtual units of memory and allocated secondvirtual units of memory is performed by a plurality of authorities inthe storage system, each authority owning a range of data and handlingdata in the range of data using first virtual units of memory and secondvirtual units of memory allocated to the authority by the memorymanager.

14. The computer-readable media of claim 8, wherein the writing operatesat a logical layer in the storage system, the defragmenting operates ata device layer, in the storage system, and the mapping translatesbetween the logical layer and the device layer.

15. A storage system, comprising:

-   NVRAM (nonvolatile random-access memory);-   solid-state storage memory; and-   a processor, to perform a method, comprising:

allocating virtual units of NVRAM with mapping of the virtual units tophysical memory;

writing data having various sizes into allocated first virtual units ofmemory each comprising a first contiguous physical addressed amount ofNVRAM having a first size, and into allocated second virtual units ofmemory each comprising an amount of NVRAM having a second, larger size,as staging for later transfer of data from the NVRAM to the solid-statestorage memory; and

defragmenting the NVRAM, to have available for the allocating, aplurality of the second virtual units of memory each comprising acontiguous physical addressed amount of NVRAM having the second, largersize.

16. The storage system of claim 15, wherein the defragmenting comprises:

relocating data in the NVRAM corresponding to a plurality of the firstvirtual units of memory, to free up contiguous space in the NVRAM.

17. The storage system of claim 15, wherein the defragmenting comprises:

relocating data in the NVRAM, to free up a plurality of contiguousspaces in the NVRAM, each comprising the contiguous physical addressedamount of NVRAM having the second, larger size.

18. The storage system of claim 15, wherein the defragmenting comprises:

relocating data in the NVRAM corresponding to portions of the secondvirtual units of memory, to free up contiguous space in the NVRAM.

19. The storage system of claim 15, wherein the first contiguousphysical addressed amount of NVRAM having the first size comprises 512bytes of NVRAM having contiguous physical addressing, the amount ofNVRAM having the second, larger size comprises eight portions of NVRAMeach having 512 bytes of NVRAM having contiguous physical addressingtotaling 4 kB (kilobytes) of NVRAM, and the contiguous physicaladdressed amount of NVRAM having the second, larger size comprises 4 kBof NVRAM having contiguous physical addressing.

20. The storage system of claim 15, wherein the writing data intoallocated first virtual units of memory and allocated second virtualunits of memory is performed in a logical layer by a plurality ofauthorities in the storage system, each authority owning a range of dataand handling data in the range of data, the defragmenting operates at adevice layer, in the storage system, and the mapping translates betweenthe logical layer and the device layer.

Detailed illustrative embodiments are disclosed herein. However,specific functional details disclosed herein are merely representativefor purposes of describing embodiments. Embodiments may, however, beembodied in many alternate forms and should not be construed as limitedto only the embodiments set forth herein.

It should be understood that although the terms first, second, etc. maybe used herein to describe various steps or calculations, these steps orcalculations should not be limited by these terms. These terms are onlyused to distinguish one step or calculation from another. For example, afirst calculation could be termed a second calculation, and, similarly,a second step could be termed a first step, without departing from thescope of this disclosure. As used herein, the term “and/or” and the “/”symbol includes any and all combinations of one or more of theassociated listed items.

As used herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”,“comprising”, “includes”, and/or “including”, when used herein, specifythe presence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof. Therefore, the terminology usedherein is for the purpose of describing particular embodiments only andis not intended to be limiting.

It should also be noted that in some alternative implementations, thefunctions/acts noted may occur out of the order noted in the figures.For example, two figures shown in succession may in fact be executedsubstantially concurrently or may sometimes be executed in the reverseorder, depending upon the functionality/acts involved.

With the above embodiments in mind, it should be understood that theembodiments might employ various computer-implemented operationsinvolving data stored in computer systems. These operations are thoserequiring physical manipulation of physical quantities. Usually, thoughnot necessarily, these quantities take the form of electrical ormagnetic signals capable of being stored, transferred, combined,compared, and otherwise manipulated. Further, the manipulationsperformed are often referred to in terms, such as producing,identifying, determining, or comparing. Any of the operations describedherein that form part of the embodiments are useful machine operations.The embodiments also relate to a device or an apparatus for performingthese operations. The apparatus can be specially constructed for therequired purpose, or the apparatus can be a general-purpose computerselectively activated or configured by a computer program stored in thecomputer. In particular, various general-purpose machines can be usedwith computer programs written in accordance with the teachings herein,or it may be more convenient to construct a more specialized apparatusto perform the required operations.

A module, an application, a layer, an agent or other method-operableentity could be implemented as hardware, firmware, or a processorexecuting software, or combinations thereof. It should be appreciatedthat, where a software-based embodiment is disclosed herein, thesoftware can be embodied in a physical machine such as a controller. Forexample, a controller could include a first module and a second module.A controller could be configured to perform various actions, e.g., of amethod, an application, a layer or an agent.

The embodiments can also be embodied as computer readable code on atangible non-transitory computer readable medium. The computer readablemedium is any data storage device that can store data, which can bethereafter read by a computer system. Examples of the computer readablemedium include hard drives, network attached storage (NAS), read-onlymemory, random-access memory, CD-ROMs, CD-Rs, CD-RWs, magnetic tapes,and other optical and non-optical data storage devices. The computerreadable medium can also be distributed over a network coupled computersystem so that the computer readable code is stored and executed in adistributed fashion. Embodiments described herein may be practiced withvarious computer system configurations including hand-held devices,tablets, microprocessor systems, microprocessor-based or programmableconsumer electronics, minicomputers, mainframe computers and the like.The embodiments can also be practiced in distributed computingenvironments where tasks are performed by remote processing devices thatare linked through a wire-based or wireless network.

Although the method operations were described in a specific order, itshould be understood that other operations may be performed in betweendescribed operations, described operations may be adjusted so that theyoccur at slightly different times or the described operations may bedistributed in a system which allows the occurrence of the processingoperations at various intervals associated with the processing.

In various embodiments, one or more portions of the methods andmechanisms described herein may form part of a cloud-computingenvironment. In such embodiments, resources may be provided over theInternet as services according to one or more various models. Suchmodels may include Infrastructure as a Service (IaaS), Platform as aService (PaaS), and Software as a Service (SaaS). In IaaS, computerinfrastructure is delivered as a service. In such a case, the computingequipment is generally owned and operated by the service provider. Inthe PaaS model, software tools and underlying equipment used bydevelopers to develop software solutions may be provided as a serviceand hosted by the service provider. SaaS typically includes a serviceprovider licensing software as a service on demand. The service providermay host the software, or may deploy the software to a customer for agiven period of time. Numerous combinations of the above models arepossible and are contemplated.

Various units, circuits, or other components may be described or claimedas “configured to” perform a task or tasks. In such contexts, the phrase“configured to” is used to connote structure by indicating that theunits/circuits/components include structure (e.g., circuitry) thatperforms the task or tasks during operation. As such, theunit/circuit/component can be said to be configured to perform the taskeven when the specified unit/circuit/component is not currentlyoperational (e.g., is not on). The units/circuits/components used withthe “configured to” language include hardware—for example, circuits,memory storing program instructions executable to implement theoperation, etc. Reciting that a unit/circuit/component is “configuredto” perform one or more tasks is expressly intended not to invoke 35U.S.C. 112, sixth paragraph, for that unit/circuit/component.Additionally, “configured to” can include generic structure (e.g.,generic circuitry) that is manipulated by software and/or firmware(e.g., an FPGA or a general-purpose processor executing software) tooperate in manner that is capable of performing the task(s) at issue.“Configured to” may also include adapting a manufacturing process (e.g.,a semiconductor fabrication facility) to fabricate devices (e.g.,integrated circuits) that are adapted to implement or perform one ormore tasks.

The foregoing description, for the purpose of explanation, has beendescribed with reference to specific embodiments. However, theillustrative discussions above are not intended to be exhaustive or tolimit the invention to the precise forms disclosed. Many modificationsand variations are possible in view of the above teachings. Theembodiments were chosen and described in order to best explain theprinciples of the embodiments and its practical applications, to therebyenable others skilled in the art to best utilize the embodiments andvarious modifications as may be suited to the particular usecontemplated. Accordingly, the present embodiments are to be consideredas illustrative and not restrictive, and the invention is not to belimited to the details given herein, but may be modified within thescope and equivalents of any appended claims.

What is claimed is:
 1. A method, comprising: allocating, by a memorymanager in a storage system, virtual units of NVRAM (nonvolatilerandom-access memory) corresponding to physical memory; writing datahaving various sizes into allocated first virtual units each comprisinga first contiguous physical addressed amount of NVRAM having a firstsize, and into allocated second virtual units of memory each comprisingan amount of NVRAM having a second size; and relocating at least some ofthe data such that a portion of the allocated second virtual units ofmemory become available for the allocating.
 2. The method of claim 1,wherein the relocating comprises: defragmenting the NVRAM via movementof some of the first virtual units of memory.
 3. The method of claim 1,wherein the second size is larger than the first size.
 4. The method ofclaim 1, wherein the relocating comprises: moving data in the NVRAMcorresponding to portions of the second virtual units of memory, to freeup contiguous space in the NVRAM.
 5. The method of claim 1, furthercomprising: tracking correspondence between the first virtual units, thesecond virtual units and physical memory addresses in the NVRAM.
 6. Themethod of claim 1, wherein the writing data into allocated first virtualunits of memory and allocated second virtual units of memory isperformed by a plurality of authorities in the storage system, eachauthority owning a range of data and handling data in the range of datausing first virtual units of memory and second virtual units of memoryallocated to the authority by the memory manager.
 7. The method of claim5, wherein the writing operates at a logical layer in the storagesystem, the relocating operates at a device layer, in the storagesystem, and the tracking translates between the logical layer and thedevice layer.
 8. A tangible, non-transitory, computer-readable mediahaving instructions thereupon which, when executed by a processor, causethe processor to perform a method comprising: allocating, by a memorymanager in a storage system, virtual units of NVRAM (nonvolatilerandom-access memory) with mapping of the virtual units to physicalmemory; writing data having various sizes into allocated first virtualunits of memory each comprising a first contiguous physical addressedamount of NVRAM having a first size, and into allocated second virtualunits of memory each comprising an amount of NVRAM having a second size;and relocating at least some of the data such that a portion of theallocated second virtual units of memory become available for theallocating.
 9. The computer-readable media of claim 8, wherein therelocating comprises: defragmenting the NVRAM via movement of some ofthe first virtual units of memory.
 10. The computer-readable media ofclaim 8, wherein the second size is larger than the first size.
 11. Thecomputer-readable media of claim 8, wherein the relocating comprises:moving data in the NVRAM corresponding to portions of the second virtualunits of memory, to free up contiguous space in the NVRAM.
 12. Thecomputer-readable media of claim 8, further comprising: trackingcorrespondence between the first virtual units, the second virtual unitsand physical memory addresses in the NVRAM.
 13. The computer-readablemedia of claim 8, wherein the writing data into allocated first virtualunits of memory and allocated second virtual units of memory isperformed by a plurality of authorities in the storage system, eachauthority owning a range of data and handling data in the range of datausing first virtual units of memory and second virtual units of memoryallocated to the authority by the memory manager.
 14. Thecomputer-readable media of claim 12, wherein the writing operates at alogical layer in the storage system, the relocating operates at a devicelayer, in the storage system, and the tracking translates between thelogical layer and the device layer.
 15. A storage system, comprising:NVRAM (nonvolatile random-access memory); solid-state storage memory;and a processor, to perform a method, comprising: allocating virtualunits of NVRAM with mapping of the virtual units to physical memory;writing data having various sizes into allocated first virtual units ofmemory each comprising a first contiguous physical addressed amount ofNVRAM having a first size, and into allocated second virtual units ofmemory each comprising an amount of NVRAM having a second size; andrelocating at least some of the data such that a portion of theallocated second virtual units of memory become available for theallocating.
 16. The storage system of claim 15, wherein thedefragmenting comprises: relocating data in the NVRAM corresponding to aplurality of the first virtual units of memory, to free up contiguousspace in the NVRAM.
 17. The storage system of claim 15, wherein thedefragmenting comprises: relocating data in the NVRAM, to free up aplurality of contiguous spaces in the NVRAM, each comprising thecontiguous physical addressed amount of NVRAM having the second, largersize.
 18. The storage system of claim 15, wherein the relocatingcomprises: defragmenting the NVRAM via movement of some of the firstvirtual units of memory.
 19. The storage system of claim 15, furthercomprising: tracking correspondence between the first virtual units, thesecond virtual units and physical memory addresses in the NVRAM.
 20. Thestorage system of claim 19, wherein the writing data into allocatedfirst virtual units of memory and allocated second virtual units ofmemory is to be performed in a logical layer by a plurality ofauthorities in the storage system, each authority owning a range of dataand handling data in the range of data, the relocating is to operate ata device layer, in the storage system, and the tracking is to translatebetween the logical layer and the device layer.